Methods of Lag-Compensation for Analyte Measurements, and Devices Related Thereto

ABSTRACT

Methods comprising applying a first analyte point measurement filter comprising: receiving, from an in vivo analyte sensor, at least a first, second, and third uncompensated analyte measurement at a first, second and third reference time; determining a first scaled rate-of-change by multiplying a first weighting coefficient and a first rate-of-change, the first rate-of-change computed between the first uncompensated analyte measurement at the first initial reference time to the second uncompensated analyte measurement at the first prior reference time; determining a second scaled rate-of-change by multiplying a second weighting coefficient and a second rate-of-change, the second rate-of-change computed between the first uncompensated analyte measurement at the first initial reference time to the third uncompensated analyte measurement at the second prior reference time; and calculating a first filter lag-compensated point measurement based on the sum of the first uncompensated analyte measurement, the first scaled rate-of-change, and the second scaled rate-of-change.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application No. 61/637,748 filed Apr. 24,2012, the disclosure of which is incorporated by reference herein in itsentirety.

INTRODUCTION

In many instances it is desirable or necessary to regularly monitor theconcentration of particular constituents in a fluid. A number of systemsare available that analyze the constituents of bodily fluids such asblood, urine and saliva. Examples of such systems conveniently monitorthe level of particular medically significant fluid constituents, suchas, for example, cholesterol, ketones, vitamins, proteins, and variousmetabolites or blood sugars, such as glucose. Diagnosis and managementof patients suffering from diabetes mellitus, a disorder of the pancreaswhere insufficient production of insulin prevents normal regulation ofblood sugar levels, requires carefully monitoring of blood glucoselevels on a daily basis. A number of systems that allow individuals toeasily monitor their blood glucose are currently available. Some ofthese systems include electrochemical biosensors, including those thatcomprise a glucose sensor that is adapted for complete or partialinsertion into a subcutaneous site within the body for the continuous orperiodic (e.g., on-demand) in vivo monitoring of glucose levels inbodily fluid (e.g., blood or interstitial fluid (ISF)) of thesubcutaneous site. ISF glucose lags in time behind blood glucose. Thatis, if the blood glucose is falling and reaches a low point, the ISFglucose will reach that low point some time later, such as 10 minutesfor example. Traditionally, the goal of analyte monitoring systems is toprovide results that approximate blood glucose concentrations sinceblood glucose concentrations better represent the glucose level in thepatient's blood.

SUMMARY

In some aspects of the present disclosure, methods of lag compensationfor analyte point measurements are provided. The methods includereceiving a series of uncompensated analyte measurements; anddetermining a first set of parameter values for an analyte pointestimate based on reference analyte measurements. The analyte pointestimate is based on a sum of an analyte point and a sum of a pluralityof scaled rates-of-changes. The analyte point corresponds tomeasurements at an initial reference time. The rates-of-changes includea first rate-of-change from the initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time.

In some aspects of the present disclosure, methods of lag compensationfor analyte rate-of-change measurements are provided. The methodsinclude receiving reference analyte measurements, and determining afirst set of parameter values for an analyte rate-of-change estimatebased on the reference analyte measurements. The analyte rate-of-changeestimate is based on a sum of a plurality of scaled rates-of-changes.The rates-of-changes include a first rate-of-change from an initialreference time to a first prior reference time, and a secondrate-of-change from the initial reference time to a second priorreference time.

In some aspects of the present disclosure, methods of lag compensationfor analyte point measurements and analyte rate-of-change measurementsare provided. The methods include receiving reference analytemeasurements, and determining a first set of parameter values for ananalyte point estimate based on the reference analyte measurements. Theanalyte point estimate is based on a sum of an analyte point and a sumof a first plurality of scaled rates-of-changes. The analyte pointcorresponds to measurements at an initial reference time. Therates-of-changes of the first plurality include a first rate-of-changefrom the initial reference time to a first prior reference time, and asecond rate-of-change from the initial reference time to a second priorreference time. The methods also include determining a second set ofparameter values for an analyte rate-of-change estimate based on thereference analyte measurements. The analyte rate-of-change estimate isbased on the sum of a second plurality of scaled rates-of-changes. Therates-of-changes of the second plurality include a third rate-of-changefrom an initial reference time to a third prior reference time, and afourth rate-of-change from the initial reference time to a fourth priorreference time.

In some aspects of the present disclosure, articles of manufacture forlag compensation of analyte point measurements are provided. Thearticles of manufacture include a machine-readable medium havingmachine-executable instructions stored thereon for lag compensation ofanalyte measurements. The instructions include instructions forreceiving reference analyte measurements, and instructions fordetermining a first set of parameter values for an analyte pointestimate based on the reference analyte measurements. The analyte pointestimate is based on a sum of an analyte point and a sum of a pluralityof scaled rates-of-changes. The analyte point corresponds tomeasurements at an initial reference time. The rates-of-changes includea first rate-of-change from the initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time.

In some aspects of the present disclosure, articles of manufacture forlag compensation of analyte rate-of-change measurements are provided.The articles of manufacture include a machine-readable medium havingmachine-executable instructions stored thereon for lag compensation ofanalyte measurements. The instructions include instructions forreceiving reference analyte measurements, and instructions fordetermining a first set of parameter values for an analyterate-of-change estimate based on the reference analyte measurements. Theanalyte rate-of-change estimate is based on a sum of a plurality ofscaled rates-of-changes. The rates-of-changes include a firstrate-of-change from an initial reference time to a first prior referencetime, and a second rate-of-change from the initial reference time to asecond prior reference time.

In some aspects of the present disclosure, articles of manufacture forlag compensation of analyte point measurements and analyterate-of-change measurements are provided. The articles of manufactureinclude a machine-readable medium having machine-executable instructionsstored thereon for lag compensation of analyte measurements. Theinstructions include instructions for receiving reference analytemeasurements, and instructions for determining a first set of parametervalues for an analyte point estimate based on the reference analytemeasurements. The analyte point estimate is based on a sum of an analytepoint and a sum of a first plurality of scaled rates-of-changes. Theanalyte point corresponds to measurements at an initial reference time.The rates-of-changes of the first plurality include a firstrate-of-change from the initial reference time to a first priorreference time and a second rate-of-change from the initial referencetime to a second prior reference time. The articles of manufacture alsoinclude instructions for determining a second set of parameter valuesfor an analyte rate-of-change estimate based on the reference analytemeasurements. The analyte rate-of-change estimate is based on the sum ofa second plurality of scaled rates-of-changes. The rates-of-changes ofthe second plurality include a third rate-of-change from an initialreference time to a third prior reference time, and a fourthrate-of-change from the initial reference time to a fourth priorreference time.

INCORPORATION BY REFERENCE

Additional embodiments of analyte monitoring systems suitable forpracticing methods of the present disclosure are described in U.S. Pat.Nos. 6,175,752; 6,134,461; 6,579,690; 6,605,200; 6,605,201; 6,654,625;6,746,582; 6,932,894; 7,090,756; 5,356,786; 6,560,471; 5,262,035;6,881,551; 6,121,009; 7,167,818; 6,270,455; 6,161,095; 5,918,603;6,144,837; 5,601,435; 5,822,715; 5,899,855; 6,071,391; 6,377,894;6,600,997; 6,514,460; 5,628,890; 5,820,551; 6,736,957; 4,545,382;4,711,245; 5,509,410; 6,540,891; 6,730,200; 6,764,581; 6,503,381;6,676,816; 6,893,545; 6,514,718; 5,262,305; 5,593,852; 6,746,582;6,284,478; 7,299,082; 7,811,231; 7,822,557; 8,106,780; 8,103,471; U.S.Patent Application Publication No. 2010/0198034; U.S. Patent ApplicationPublication No. 2010/0324392; U.S. Patent Application Publication No.2010/0326842 U.S. Patent Application Publication No. 2007/0095661; U.S.Patent Application Publication No. 2008/0179187; U.S. Patent ApplicationPublication No. 2008/0177164; U.S. Patent Application Publication No.2011/0120865; U.S. Patent Application Publication No. 2011/0124994; U.S.Patent Application Publication No. 2011/0124993; U.S. Patent ApplicationPublication No. 2010/0213057; U.S. Patent Application Publication No.2011/0213225; U.S. Patent Application Publication No. 2011/0126188; U.S.Patent Application Publication No. 2011/0256024; U.S. Patent ApplicationPublication No. 2011/0257495; U.S. Patent Application Publication No.2012/0157801; U.S. Patent Application Publication No. 2012/024544; U.S.Patent Application Publication No. 2012/0323098; U.S. Patent ApplicationPublication No. 2012/0157801; U.S. Patent Application Publication No.2010/0213057; U.S. Patent Application Publication No. 2011/0193704; U.S.Provisional Patent Application No. 61/582,209; and U.S. ProvisionalPatent Application Publication No. 61/581,065; the disclosures of eachof which are incorporated herein by reference in their entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description of various embodiments of the present disclosureis provided herein with reference to the accompanying drawings, whichare briefly described below. The drawings are illustrative and are notnecessarily drawn to scale. The drawings illustrate various embodimentsof the present disclosure and may illustrate one or more embodiment(s)or example(s) of the present disclosure in whole or in part. A referencenumeral, letter, and/or symbol that is used in one drawing to refer to aparticular element may be used in another drawing to refer to a likeelement.

FIG. 1 illustrates an example scatter plot of the difference betweensensor glucose (CGM) and blood glucose (BG) versus sensor glucose rate.

FIG. 2 illustrates graphs of example analyte measurement plots andcorresponding calibration factors based on the relationship shown inFIG. 1.

FIG. 3 illustrates flowcharts for a method of lag compensation ofanalyte rate-of-change measurements, according to one embodiment.

FIG. 4 illustrates flowcharts for a method of lag compensation ofanalyte point measurements, according to one embodiment.

FIG. 5 illustrates a flowchart for a method of lag compensation ofanalyte point measurements and analyte rate-of-change measurements,according to one embodiment.

FIG. 6 illustrates a flowchart for a method of lag-compensation ofanalyte rate of change measurements with multiple analyte rate-of-changefilters, according to one embodiment.

FIG. 7 illustrates a flowchart for a method of lag-compensation ofanalyte point measurements with multiple analyte point filters,according to one embodiment.

FIG. 8 illustrates a flowchart for a method of lag-compensation ofanalyte point and rate-of-change measurements with multiple analytepoint filters and multiple analyte rate-of-change filters, according toone embodiment.

FIG. 9 illustrates a graph of an example analyte measurement plot havingdropouts.

FIG. 10 illustrates a flowchart for a method of lag compensation ofanalyte rate-of-change measurements with multiple banks, according toone embodiment.

FIG. 11 illustrates a flowchart for a method of lag compensation ofanalyte point measurements with multiple banks, according to oneembodiment.

FIG. 12 illustrates a flowchart for a method of lag compensation ofanalyte point and rate-of-change measurements with multiple banks ineach, according to one embodiment.

FIG. 13 shows an analyte (e.g., glucose) monitoring system, according toone embodiment.

FIG. 14 is a block diagram of the data processing unit 1402 shown inFIG. 13 in accordance with one embodiment.

FIG. 15 is a block diagram of an embodiment of a receiver/monitor unitsuch as the primary receiver unit 1404 of the analyte monitoring systemshown in FIG. 13.

Before the embodiments of the present disclosure are described, it is tobe understood that the present disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the embodiments of the present disclosurewill be limited only by the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimits of that range is also specifically disclosed. Each smaller rangebetween any stated value or intervening value in a stated range and anyother stated or intervening value in that stated range is encompassedwithin the present disclosure. The upper and lower limits of thesesmaller ranges may independently be included or excluded in the range,and each range where either, neither or both limits are included in thesmaller ranges is also encompassed within the present disclosure,subject to any specifically excluded limit in the stated range. Wherethe stated range includes one or both of the limits, ranges excludingeither or both of those included limits are also included in the presentdisclosure.

In the description of the present disclosure herein, it will beunderstood that a word appearing in the singular encompasses its pluralcounterpart, and a word appearing in the plural encompasses its singularcounterpart, unless implicitly or explicitly understood or statedotherwise. Merely by way of example, reference to “an” or “the”“analyte” encompasses a single analyte, as well as a combination and/ormixture of two or more different analytes, reference to “a” or “the”“concentration value” encompasses a single concentration value, as wellas two or more concentration values, and the like, unless implicitly orexplicitly understood or stated otherwise. Further, it will beunderstood that for any given component described herein, any of thepossible candidates or alternatives listed for that component, maygenerally be used individually or in combination with one another,unless implicitly or explicitly understood or stated otherwise.Additionally, it will be understood that any list of such candidates oralternatives, is merely illustrative, not limiting, unless implicitly orexplicitly understood or stated otherwise.

Various terms are described below to facilitate an understanding of thepresent disclosure. It will be understood that a correspondingdescription of these various terms applies to corresponding linguisticor grammatical variations or forms of these various terms. It will alsobe understood that the present disclosure is not limited to theterminology used herein, or the descriptions thereof, for thedescription of particular embodiments. Merely by way of example, thepresent disclosure is not limited to particular analytes, bodily ortissue fluids, blood or capillary blood, or sensor constructs or usages,unless implicitly or explicitly understood or stated otherwise, as suchmay vary. The publications discussed herein are provided solely fortheir disclosure prior to the filing date of the application. Nothingherein is to be construed as an admission that the embodiments of thepresent disclosure are not entitled to antedate such publication byvirtue of prior invention. Further, the dates of publication providedmay be different from the actual publication dates which may need to beindependently confirmed.

DETAILED DESCRIPTION

In general, the present disclosure relates to method of providing ananalyte estimate, such as a glucose estimate, for a continuous glucosemonitoring (CGM) system—e.g., such as the FreeStyle Navigator (FSN) CGMsystem manufactured by Abbott Diabetes Care Inc. For example, such CGMsystems include an analyte sensor that may be fully or partiallyimplanted in the subcutaneous tissue of a subject and coming in contactwith and monitoring the analyte level of biological fluid, such asinterstitial fluid present in the subcutaneous tissue. In some instancesduring use, the system may experience a lag between the interstitialfluid-to-blood analyte levels, which may present an artificial source oferror for CGM systems. For example, if the blood glucose is falling andreaches a low point, the ISF glucose will reach that low point some timelater, such as 10 minutes for example. Therefore, it is desirable toprovide results that approximate blood glucose concentrations sinceblood glucose concentrations better represent the subject's glucoselevel at any point in time.

In some aspects of the present disclosure, methods are provided thatcompensate for a lag in glucose level measurements that may beexperienced in such systems. This method of lag-compensation is based onthe principle that as the rate of change of the glucose level increases,the level of lag in the glucose level in the interstitial fluid to theglucose in the blood will also increase. Accordingly, this method seeksto determine the rate of change of the blood level for two time periodsjust prior to a reference time and based on the difference in the rateof change for the two time periods will apply a different level of lagcorrection to the time points. If a first time period has a lower rateof change than a second time period, then the factor of lag compensationapplied to the first time period may be lower than the factor of lagcompensation applied to the second time period. Based on these scaledrates of change, the glucose measurements are compensated for at thedifferent time points in a relative manner to the determined factor ofrate of change.

For example, the methods include receiving a series of uncompensatedglucose measurements and determining a first set of parameter values foran glucose level estimate based on reference analyte measurements tocompensate for a lag in glucose level measurements. The glucose levelestimate is based on a sum of a glucose level and a sum of a pluralityof scaled rates-of-changes. The analyte point corresponds tomeasurements at an initial reference time. The rates-of-changes includea first rate-of-change from the initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time. A first set of weightingcoefficients are then derived from the first set of parameter values andlag-compensated glucose level measurements are subsequently calculatedfrom the uncompensated glucose measurements by applying the first set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, the first priorreference time, and the second prior reference time of the firstparameter values.

FIG. 1 illustrates an example scatter plot of the difference that may beexperienced between sensor glucose (CGM) and blood glucose (BG) versussensor glucose rate. The difference 105 between the CGM glucose andreference BG measurement (e.g. capillary finger sticks, venous YSImeasurements, or other standard or reference measurement) is representedon the vertical axis, while the CGM rate 110 is represented on thehorizontal axis. As shown, the discrepancy between the CGM glucose andreference BG changes with respect to the CGM rate. In the example shown,the difference 105 is approximately zero when the CGM rate is zero, asrepresented by point 115. Thus, when the CGM glucose is not changing,the glucose discrepancy is approximately zero or otherwise minimal. Asthe CGM rate increases positively, the discrepancy between the CGMglucose and BG increases, with the CGM glucose becoming smaller withrespect to the BG, and yielding a negative difference as shown.Similarly, as the CGM rate increases negatively, the discrepancy betweenthe CGM glucose and BG increases, with the CGM glucose becoming largerwith respect to the BG, and yielding a positive difference as shown.

FIG. 2 illustrates graphs of example analyte measurement plots andcorresponding calibration factors, called sensitivity, based on therelationship shown in FIG. 1. The bottom sub-graph 200 shows glucosemeasurement values along the vertical axis (represented in milligramsper deciliter (mg/dL)) and time along the horizontal axis (representedin hours). The sub graph 200 shows uncompensated CGM measurements 205that has been calibrated to match steady-state reference glucose values,first order lag-compensated measurements 210, and fingerstick referencemeasurement 215 and YSI reference measurement 220. The top sub-graphillustrates an example computed sensitivity for the uncompensated CGMmeasurement (0th order model, each value generated by taking the ratiobetween local CGM and each reference measurement) 225 and an examplecomputed sensitivity for the first order lag-compensated measurement 230(where each value is generated by taking the first order lag-compensatedlocal CGM and each reference measurement). As rates of change move awayfrom zero, the 0th order model 225 results in a predictable error thatpersists until rate returns to zero. The 1st order model correctedsensitivity 230 however, remains closer to the true steady-state valueexcept for two regions, where the larger rates of changes exist. The0^(th) order sensitivity 225 is biased slightly lower to facilitatemanual comparison against the 1^(st) order sensitivity 230.

Exemplary methods according to certain embodiments of lag compensationof analyte rate-of-change measurements and a method of lag compensationof analyte point measurements, are illustrated in FIGS. 3 and 4,respectively. The term “analyte point” is used herein to refer generallyto the analyte measurement's magnitude or value. The term “analyterate-of-change” is used herein to refer generally to the rate at whichthe analyte measurements are changing. While FIGS. 3 and 4 are describedtogether below, the two methods are independent of one another. In otherwords, either method may be performed with or without the performance ofthe other method.

At blocks 305 and 405, reference analyte measurements are received. Forexample, the reference analyte measurements may be originally derivedfrom a preexisting study and data. This data may contain, for example, arelatively frequent and accurate reference analyte measurements thathave been collected over a given time period. Examples of referenceanalyte measurements include venous glucose measurement using a YSIinstrument, or capillary BG measurement using a BG meter.

Referring to FIG. 3, the parameter values for an analyte rate-of-changeestimate are determined based on the reference analyte measurements. Theanalyte rate-of-change estimate is based on a sum of a plurality ofscaled rates-of-changes. The rates-of-changes include a rate-of-changefrom the initial reference time to a first prior reference time, andanother rate-of-change from the initial reference time to a second priorreference time that is different than the first prior reference time. Itshould be appreciated that while two rates-of-changes are described, theanalyte rate-of-change estimate may include more than tworates-of-changes, such as three, four, five, or more rates of changes,in other embodiments. The parameter values may include, for example,scalars for each of the rates-of-changes, as well as the prior referencetimes for each of the rate-of-changes.

For example, in one embodiment, a glucose rate-of-change estimate isrepresented by a scaled sum of rates-of-changes of CGM values, which mayalso be referred to herein as a scaled sum of 2 first (backwards)differences of CGM measurements. The glucose rate-of-change estimate maybe represented as follows:

$\begin{matrix}{{{\hat{\overset{.}{G}}}_{b}(k)}:={{\frac{c_{1}}{N_{1}}\left\lbrack {{y(k)} - {y\left( {k - N_{1}} \right)}} \right\rbrack} + {\frac{c_{2}}{N_{2}}\left\lbrack {{y(k)} - {y\left( {k - N_{2}} \right)}} \right\rbrack}}} \\{= {{\left\lbrack {\frac{c_{1}}{N_{1}} + \frac{c_{2}}{N_{2}}} \right\rbrack {y(k)}} + {\left\lbrack {- \frac{c_{1}}{N_{1}}} \right\rbrack {y\left( {k - N_{1}} \right)}} + {\left\lbrack {- \frac{c_{2}}{N_{2}}} \right\rbrack {y\left( {k - N_{2}} \right)}}}} \\{= {{d_{0}{y(k)}} + {d_{1}{y\left( {k - N_{1}} \right)}} + {d_{2}{y\left( {k - N_{2}} \right)}} - d_{0}}} \\{= {d_{1} + d_{2}}}\end{matrix}$

where k is the sample time index of the sensor data, y is the calibratedsensor measurement, c₁ and c₂ are scalars, and N₁ and N₂ are time delayindices. Scalar, c₁, is multiplied by a first rate-of-change between twomeasurements at an N₁ time interval apart; and scalar, c₂, is multipliedby a second rate-of-change between two measurements at an N₂ timeinterval apart. In this way, two first order components with differenttime intervals (N₁ or N₂) between each corresponding raw data pairs(y(k) and y(k−N₁) or y(k) and y(k−N₂)) may be found and permit thecapturing of at least two dominant modes that govern the dynamic lagrelationship. As shown, the values of the predetermined, fixed weightingcoefficients—d₀, d₁, and d₂—will be based on the values selected for theparameters—c₁, c₂, N₁, N₂—of the glucose rate-of-change estimate.

Referring to FIG. 4, the parameter values for the analyte pointestimate, such as glucose level estimate, are determined based on thereference analyte measurements. The analyte point estimate is based on asum of an analyte point and a sum of a plurality of scaledrates-of-changes. The analyte point corresponds to measurements at aninitial reference time. The rates-of-changes include a rate-of-changefrom the initial reference time to a first prior reference time, andanother rate-of-change from the initial reference time to a second priorreference time that is different than the first prior reference time. Itshould be appreciated that while two rates-of-changes are described, theanalyte point estimate may include more than two rates-of-changes, suchas three, four, five, or more rates of changes. The parameter values mayinclude, for example, scalars for each of the rates-of-changes, as wellas the prior reference times for each of the rate-of-changes.

For example, in one embodiment, a glucose point estimate is representedby the following sum of an analyte point and scaled sum ofrates-of-changes of CGM values, which may also be referred to herein asa scaled sum of 2 first (backwards) differences of CGM measurements. Forinstance, the glucose point estimate may be a sum of the latest valueplus a sum of 2 scaled first differences. The glucose point estimate maybe represented as follows:

$\begin{matrix}{{{\hat{G}}_{b}(k)}:={{y(k)} + {\frac{a_{1}}{N_{1}}\left\lbrack {{y(k)} - {y\left( {k - N_{1}} \right)}} \right\rbrack} + {\frac{a_{2}}{N_{2}}\left\lbrack {{y(k)} - {y\left( {k - N_{2}} \right)}} \right\rbrack}}} \\{= {{\left\lbrack {1 + \frac{a_{1}}{N_{1}} + \frac{a_{2}}{N_{2}}} \right\rbrack {y(k)}} + {\left\lbrack {- \frac{a_{1}}{N_{1}}} \right\rbrack {y\left( {k - N_{1}} \right)}} + {\left\lbrack {- \frac{a_{2}}{N_{2}}} \right\rbrack {y\left( {k - N_{2}} \right)}}}} \\{= {{b_{0}{y(k)}} + {b_{1}{y\left( {k - N_{1}} \right)}} + {b_{2}{y\left( {k - N_{2}} \right)}}}}\end{matrix}$

where k is the sample time index of the sensor data, y is the calibratedsensor measurement, a₁ and a₂ are scalars, and N₁ and N₂ are time delayindices. Scalar, a₁, is multiplied by a first rate-of-change between twomeasurements at an N₁ time interval apart. Scalar, a₂, is multiplied bya second rate-of-change between two measurements at an N₂ time intervalapart. In this way, two first order components with different timeintervals (N₁ or N₂) between each corresponding raw data pairs (y(k) andy(k−N₁) or y(k) and y(k−N₂)) may be found and permit the capturing of atleast two dominant modes that govern the dynamic lag relationship. Asshown, the values of the predetermined, fixed weighting coefficients—b₀,b₁, and b₂—will be based on the values selected for the parameters—a₁,a₂, N₁, N₂—of the glucose point estimate.

Referring back to FIGS. 1 and 2, while a single 1st order model seems tosomewhat reduce the correlation between CGM-to-BG discrepancy and ratein some cases, certain fast excursions demonstrate up to two temporalregions where neither a 0th order nor a 1^(st) order model canadequately predict the blood-to-sensor relationship.

In some aspects of the present disclosure, however, accurate predictionsof the blood-to-sensor relationship during such fast excursions in thetwo temporal regions may be provided. With linear time invariant (LTI)models, for example, the transfer function from blood to sensor glucosemay be viewed as predominantly first order low pass filter, but theremay be one or more near pole-zero cancellations that do not contributeto any measurable sensor signal unless the blood glucose excursioncontains the right frequency content. With Finite Impulse Response (FIR)LTI models, for example, taking the sum of more than one (e.g., two asshown in one embodiment) 1st order “rates” may permit similar behaviorin that when the blood glucose excursion contains frequency contentsthat are slower than both “rate” calculations, the output of the modelis essentially identical to a single 1st order model. On the other hand,when the blood glucose excursion contains frequency contents that are inbetween the “bandwidth” of the two components, the output of theembodiments described herein will be different from the single 1st ordermodel. The embodiments described herein provide accurate outputsrepresenting the blood-to-sensor relationship during such fastexcursions.

Parameter values may be selected to optimize the analyte point andrate-of-change estimates described for FIGS. 3 and 4. For example, theparameter values for the analyte point and rate-of-change estimates maybe determined by calculating error metrics. Similarly, at block 310 ofFIG. 3, error metrics are calculated for a plurality of combinations ofvalues as parameters in the analyte rate-of-change estimate. A set ofparameter values are then selected based on the calculated errormetrics, as represented at block 315.

At block 410 of FIG. 4, error metrics are calculated for a plurality ofcombinations of values as parameters in the analyte point estimate. Aset of parameter values are then selected based on the calculated errormetrics, as represented at block 415.

The parameter values of the analyte point and rate-of-change estimatesmay be synthesized using a development set that contains a relativelyfrequent and accurate reference analyte measurements (e.g., thereference analyte measurements provided in blocks 305 and 405 of FIGS. 4and 5, respectively). The number of terms in the estimation, as well asthe associated delays and coefficients, may be chosen, for example,using the following example method. It should be appreciated that thefollowing optimization example can be performed separately for analytepoint and rate-of-change estimates to yield optimized parameters values(e.g., scalars a₁, a₂; scalars c₁, c₂; time delays (e.g., N₁, N₂). Itshould also be appreciated that while in some embodiments, in-vivoreference glucose from a subject may be enough to perform the entireprocess described in FIGS. 3 and 4, the preferred embodiment is onewhere the synthesis is performed offline based on population sensor andreference glucose data, and the final steps 325 and 425 are performed toeach patient's in-vivo sensor glucose data. The example optimizationmethod is as follows:

-   -   1. Select number of terms in the model (e.g. two terms, with        unknown time delays N₁ and N₂).    -   2. Define search space of time delays (e.g. N₁ in integer        increments from 1 to 40 minutes). This encompasses every        combination of delays for every term, subject to the constraint        (N_(x)<N_(y) for x<y) (e.g. N₂ in integer increments up to 45        minutes, and N₁<N₂).    -   3. Iterate through search space of delays. For each specific        delay combination,        -   a. Accumulate all reference data points (e.g. reference            glucose readings for point filters or first backwards            difference of reference data readings for rate filters)        -   b. Accumulate all calibrated sensor data points required to            estimate each reference data point. (e.g. y(k), y(k−N₁),            y(k−N₂) to estimate the reference value at time k)        -   c. Apply an optimization routine (e.g. least squares error            (LS) fit, etc.) that determines the coefficients that            minimize an error function between the estimated value and            the references. It should be appreciated that any variety of            optimization routines may be used for the error metric            calculation. In one embodiment, using LS fit, coefficients            may be chosen that minimize the sum of squared error between            the reference glucose value and the estimated glucose value.            This would yield the following example cost function which            could then be fed into an optimization routine.

J:=Σ(Ĝ _(B) −G _(B))²

-   -   -   d. Record overall error metric (e.g. Sum of Squared Errors            in the case of LS) for this specific delay combination, as            well as the resulting optimal scalars (e.g. a₁ and a₂, or c₁            and c₂ for each N₁ and N₂ combination in the search space            defined in step 2).

    -   4. Choose the delay combination and its associated coefficients        by an external metric. For example, in one embodiment, the        combination with the lowest error metric is chosen.

After selecting the parameter values, the weighting coefficients for anestimate may be calculated. For example, weighting coefficients for theanalyte rate-of-change estimate may then be derived based on theselected parameter values, as represented at block 320. The weightingcoefficients are then implemented in a filter that may be used tocalculate lag-compensated rate-of-change estimates using theuncompensated analyte measurements (e.g., sensor glucose measurements),as represented by block 325. For example, the weighting coefficients d₀,d₁, and d₂ (derived based on the values selected for the parameters—c₁,c₂, N₁, N₂) may be applied to corresponding data (e.g., uncompensatedanalyte measurements) received at the initial reference time (e.g., themost recent data available), the first prior reference time N₁, and thesecond prior reference time N₂, respectively.

Referring to FIG. 4, weighting coefficients for the analyte pointestimate may then be derived based on the selected parameter values, asrepresented at block 420. The weighting coefficients are thenimplemented in a filter that may be used to calculate lag-compensatedpoint estimates using the uncompensated analyte measurements (e.g.,interstitial glucose measurements), as represented by block 425. Forexample, the weighting coefficients b₀, b₁, and b₂ (derived based on thevalues selected for the parameters—a₁, a₂, N₁, N₂) may be applied tocorresponding data (e.g., uncompensated analyte measurements) receivedat the initial reference time (e.g., the most recent data available),the first prior reference time N₁, and the second prior reference timeN₂, respectively.

Uncompensated analyte measurements may be received from, for example,interstitial glucose measurements. For instance, a transcutaneouslyimplanted sensor may communicate uncompensated analyte measurements to adata processing device (e.g., analyte monitoring device) implementingthe filter. In one embodiment, the implanted sensor is implanted in thesubcutaneous tissue and provides uncompensated analyte measurementscontinuously to an analyte monitoring device (e.g., such as incontinuous glucose monitoring (CGM) systems). In another embodiment, theimplanted sensor may provide uncompensated analyte measurementsintermittently, such as periodically or on demand (e.g., such as inglucose-on-demand (GoD) systems).

It should be appreciated that the initial reference time in the analytepoint estimate and the analyte rate-of-change estimate described above(e.g., the glucose point and rate-of-change estimates described above)may correspond to the most recent data acquired in some instances; oralternatively, to some delayed time from the most recent data. Thus, forexample, the glucose point estimate may be more generally represented bythe following:

${{\hat{G}}_{b}(k)} = {{y\left( {k - N_{0}} \right)} + {\frac{a_{1}}{N_{1} - N_{0}}\left\lbrack {{y\left( {k - N_{0}} \right)} - {y\left( {k - N_{1}} \right)}} \right\rbrack} + {\frac{a_{2}}{N_{2} - N_{0}}\left\lbrack {{y\left( {k - N_{0}} \right)} - {y\left( {k - N_{2}} \right)}} \right\rbrack}}$

wherein N₀ is an initial reference time, and the other parameter valuessimilar to those previously described. Thus, after optimized parametervalues have been selected and corresponding weighting coefficientscalculated, lag-compensated point measurements may be calculated via theglucose point estimate, as shown below:

Ĝ ₁(k)=b ₀ y(k−N ₀)+b ₁ y(k−N ₁)+b ₂ y(k−N ₂)

The time delay to the first raw signal, N₀, may be chosen to be 0 inorder to take advantage of the latest available measurement, forexample. The other time delays, N₁ and N₂, may vary depending onapplication. N₁ and N₂ may be two different numbers in the order of 1 to45 minutes, for example, but should not be interpreted as limited tosuch a time range.

Thus, the weighting coefficients b₀, b₁, and b₂ (derived based on thevalues selected for the parameters—a₁, a₂, N₁, N₂) may be applied tocorresponding data at the initial reference time N₀, the first priorreference time N₁, and the second prior reference time N₂, respectively.

Similarly, the glucose rate-of-change estimate at any sample instance kmay be more generally represented by the following, whose constants mayhave a different value:

${{\hat{\overset{.}{G}}}_{b}(k)} = {{\frac{c_{1}}{N_{1} - N_{0}}\left\lbrack {{y\left( {k - N_{0}} \right)} - {y\left( {k - N_{1}} \right)}} \right\rbrack} + {\frac{c_{2}}{N_{2} - N_{0}}\left\lbrack {{y\left( {k - N_{0}} \right)} - {y\left( {k - N_{2}} \right)}} \right\rbrack}}$

After optimized parameter values have been selected and correspondingweighting coefficients calculated, lag-compensated rate-of-changemeasurements may be calculated via the glucose rate-of-change estimate,as shown below:

Ĝ ₁(k)=d ₀ y(k−N ₀)+d ₁ y(k−N ₁)+d ₂ y(k−N ₂)

Thus, for example, the weighting coefficients d₀, d₁, and d₂ (derivedbased on the values selected for the parameters—c₁, c₂, N₁, N₂) may beapplied to corresponding data at the initial reference time N₀, thefirst prior reference time N₁, and the second prior reference time N₂,respectively.

In some aspects of the present disclosure, methods are provided thatinclude both lag compensation of analyte point measurements and lagcompensation of analyte rate-of-change measurements. For example, FIG. 5illustrates a flowchart for a method of lag compensation of analytepoint measurements and analyte rate-of-change measurements, according toone embodiment. The method includes common aspects to both methodsabove, and thus for the sake of clarity and brevity, common aspects willnot be described in great detail again.

At block 505, reference analyte measurements are received. Again, thereference analyte measurements may be provided by a development set, forexample. Parameter values may be selected to optimize the analyte pointestimate and analyte rate-of-change estimate. For example, the parametervalues for the analyte point estimate and analyte rate-of-changeestimate may be determined by calculating error metrics.

Again, the analyte point estimate is based on a sum of an analyte pointand a sum of a plurality of scaled rates-of-changes. The analyte pointcorresponds to measurements at an initial reference time. Therates-of-changes include a rate-of-change from the initial referencetime to a first prior reference time, and another rate-of-change fromthe initial reference time to a second prior reference time that isdifferent than the first prior reference time. Again, it should beappreciated that while two rates-of-changes are described, the analytepoint estimate may include more than two rates-of-changes, such asthree, four, five, or more rates of changes. The parameter values mayinclude, for example, scalars for each of the rates-of-changes, as wellas the prior reference times for each of the rate-of-changes.

Again, the analyte rate-of-change estimate is based on a sum of aplurality of scaled rates-of-changes. The rates-of-changes include arate-of-change from the initial reference time to a first priorreference time, and another rate-of-change from the initial referencetime to a second prior reference time that is different than the firstprior reference time. Again, it should be appreciated that while tworates-of-changes are described, the analyte rate-of-change estimate mayinclude more than two rates-of-changes, such as three, four, five, ormore rates of changes, in other embodiments. The parameter values mayinclude, for example, scalars for each of the rates-of-changes, as wellas the prior reference times for each of the rate-of-changes. In oneembodiment, the prior reference times in the analyte point estimate arethe same as the prior reference times in the analyte rate-of-changeestimate. In other embodiment, the prior reference times may differ.

Parameter values may be selected to optimize the analyte point estimateand analyte rate-of-change estimate. For example, the parameter valuesfor the analyte point estimate and analyte rate-of-change estimate maybe determined by calculating error metrics. At block 510, error metricsare calculated for a plurality of combinations of values as parametersin an analyte point estimate and analyte rate-of-change estimate. A setof parameter values for the analyte point estimate and a set ofparameter values for the analyte rate-of-change estimate are thenselected based on the calculated error metrics, as represented at block520. Again, as previously described, the error metrics may be generatedusing various optimization routines (e.g., by calculating asum-of-squared-errors, etc.). Furthermore, in one embodiment, theparameter values may be selected based on the smallest error metric.

After selecting the parameter values, a set of weighting coefficientsfor each estimate may be derived for the analyte point andrate-of-change estimates based on the selected sets of parameters, asrepresented by block 530. The sets of weighting coefficients are thenimplemented in a point filter and rate-of-change filter that may be usedto calculate lag-compensated point and rate-of-change measurements byapplying the corresponding sets of weighting coefficients touncompensated analyte measurements (e.g., interstitial glucosemeasurements) received at the corresponding times of the filter (e.g.,the most recent time and the selected time indices (prior referencetimes), as represented by block 535. Again, in one embodiment, the priorreference times in the analyte point estimate may be the same as theprior reference times in the analyte rate-of-change estimate. In otherembodiments, the prior reference times may differ.

Multiple Filters

In some aspects of the present disclosure, multiple filters may beimplemented. For example, multiple analyte point filters and/or multipleanalyte rate-of-change filters may be implemented in parallel to enabledifferent possible outputs.

While the optimized analyte estimates described above providesignificant advantages, the method described above utilizes a specificnumber of sensor data points (e.g., in the example embodiment shownabove, three data points are utilized—one at the initial reference timeN₀, one at the first prior reference time N₁, and another at the secondprior reference time N₂) to estimate the point and rate-of-change valuesof blood glucose at any given time. However, if any of the time indices(e.g., N0, N1, or N2) used contains invalid and/or unavailable data,then no output can be calculated, or may be difficult to determineaccurately. As a result, data availability of a CGM device using thismethod may be low in some instances.

In one aspect of the present disclosure, parallel filters are providedto permit a robust estimation that is less susceptible to invalid and/orunavailable data. The parallel filters provide additional flexibilitywhen invalid and/or unavailable data is present at a given time. Forexample, another parallel filter may be used for the lag-compensatedoutput, or combinations of filters may be used to generate thelag-compensated output (e.g., by taking the average of any of thefilters that generate an output at any given time). For example, thefollowing may be implemented to represent 3 parallel glucoserate-of-change filters:

Ĝ ₁(k)=d _(0,1) y(k)+d _(1,1) y(k−N _(1,1))+d _(2,1) y(k−N _(2,1))

Ĝ ₂(k)=d _(0,2) y(k)+d _(1,2) y(k−N _(1,2))+d _(2,2) y(k−N _(2,2))

Ĝ ₃(k)=d _(0,3) y(k)+d _(1,3) y(k−N _(1,3))+d _(2,3) y(k−N _(2,3))

where k is the sample time index of the sensor data; y is the calibratedsensor measurement; N_(1,1) and N_(2,1) are time delay indices for thefirst filter, and d_(0,1), d_(1,1), and d_(2,1) are the weightingcoefficients for the first filter (e.g. derived from values selected forcorresponding parameter values—c_(1,1), c_(2,1), N_(1,1), N_(2,1)—of thefirst analyte rate-of-change estimate); N_(1,2) and N_(2,2) are timedelay indices for the second filter, and d_(0,2), d_(1,2), and d_(2,2)are the weighting coefficients for the second filter (e.g. derived fromvalues selected for corresponding parameters—c_(1,2), c_(2,2), N_(1,2),N_(2,2)—of the second analyte rate-of-change estimate); and N_(1,3) andN_(2,3) are time delay indices for the third filter, and d_(0,3),d_(1,3), and d_(2,3) are the weighting coefficients for the third filter(e.g. derived from values selected for corresponding parameters—c_(1,3),c_(2,3), N_(1,3), N_(2,3)—of the third analyte rate-of-change estimate).It should be appreciated that while three filters are shown in theexample embodiment, any other number of filters may be implemented inother embodiments—e.g., 2 filters, 4 filters, 5 filters, etc.

The parameter values for the first, second, and third analyterate-of-change estimates may be selected similarly as discussed abovefor the single filter. For example, error metrics may be similarlycalculated for a plurality of combinations of values as parameters inthe analyte rate-of-change estimates, and the parameter values selectedbased on the calculated error metrics. For example, in one embodiment,the first filter may be associated with a better error metric (e.g.,smaller error metric) than the second filter, which is associated with abetter error metric than the third filter.

By designing the parallel filter elements such that some of the timeindex triplets across the three elements—(N_(1,1), N_(2,1), N_(3,1)),(N_(1,2), N_(2,2), N_(3,2)), (N_(1,3), N_(2,3), N_(3,3)) do not point tothe same data, the filter can be made robust to intermittent missingdata. Choosing staggered delays (e.g. ensuring that N₁, N₂, N₃ areunique for each filter) ensures that single invalid and/or unavailabledata points will not cause all the filters to fail simultaneously. Amissing data point may cause individual filters to fail, but the overallfilter bank can still provide a final value. Note that the most recentdata used in the parallel filter elements shown in the example above usea common point referring to the latest available value at any time, orput another way, the most recently received. In other embodiments, datarobustness can be improved if the “latest point” (or most recentlyreceived) in the parallel filter elements is also staggered. However,depending on the application, it may be undesirable in some instances tocompute any rate estimate in the absence of latest data. The exclusionof data staggering for the latest point is only one embodiment and isnot to be implied to be a limitation of the present disclosure. Forexample, in some embodiments, some, but not all, of the time delayindices (prior reference times) of two filters may be the same. Forexample, in the embodiment above, the first and second filter may havethe same first prior reference time (N₁ time index), but have adifferent second prior reference time (N₂ time index), or vice versa.Thus, the sets of parameter values for two filters may point todifferent sets of prior reference times for the outputs despite having acommon prior reference time. This concept is also applicable when thereare three or more filters present, and is also similarly applicable toanalyte point estimates.

As uncompensated analyte measurements are received, which may includeinvalid and/or unavailable data intermittently, the blood glucoserate-of-change estimate may then be computed based on the output of oneor more filters to generate lag-compensated rate-of-change measurements.For example, in one embodiment, lag-compensated rate-of-changemeasurements may be calculated, for example, as the average of anycombination of the calculations of the filters that generates a resultat any given time k. In another embodiment, lag compensatedrate-of-change measurements may be chosen in an hierarchical order—e.g.,from the first filter associated with the best error metric if validdata is available for the first filter; from the second filterassociated with the second best error metric if valid data is notavailable for the first filter, but available for the second filter; andfrom the third filter with third best error metric if valid data is notavailable for the first and second filters, but available for the thirdfilter. It should be appreciated that the preceding is exemplary, andthat the lag-compensated rate-of-change measurements may be calculatedfrom the three parallel filters in other various combinations, averages,weighted sums, etc.

In some embodiments, the time indices for the filters may be based onexpected duration of data unavailability. For example, in oneembodiment, the first filter is picked following the method outlinedabove for the single filter. The time index set for the second filter ispicked such that at least one of the time indices (prior referencetimes) is different from that of the first set, in a manner which allowsfor that time index to be far enough from the perspective of expecteddata unavailability duration, and such that there exist an optimalparameter set that allows the glucose rate-of-change estimates to beviable from the perspective of metrics outlined in the single filterexample.

For example, in one embodiment, two samples may be a likely duration ofmissing data that needs to be mitigated for. Then, at least one timedelay index (prior reference time) in the second filter is set to be twosamples away from that of the first filter. In addition, the resultingoptimal parameter combination results in a glucose rate-of-changeestimate that generate a similar performance as determined by theoptimization procedure outlined in the single filter embodiment.

A similar analysis can be made for the analyte point estimate (e.g.,glucose point estimate), where an array of parallel filter elements isused, and then one or more available outputs may be used. For example,the following may be implemented to represent 3 parallel glucose pointfilters:

Ĝ ₁(k)=b _(0,1) y(k)+b _(1,1) y(k−N _(1,1))+b _(2,1) y(k−N _(2,1))

Ĝ ₂(k)=b _(0,2) y(k)+b _(1,2) y(k−N _(1,2))+b _(2,2) y(k−N _(2,2))

Ĝ ₃(k)=b _(0,3) y(k)+b _(1,3) y(k−N _(1,3))+b _(2,3) y(k−N _(2,3))

where k is the sample time index of the sensor data; y is the calibratedsensor measurement; N_(1,1) and N_(2,1) are time delay indices for thefirst filter, and b_(0,1), b_(1,1), and b_(2,1) are the weightingcoefficients for the first filter (e.g., derived from values selectedfor corresponding parameter values—a_(1,1), a_(2,1), N_(1,1), N_(2,1)—ofthe first analyte rate-of-change estimate); N_(1,2) and N_(2,2) are timedelay indices for the second filter, and b_(0,2), b_(1,2), and b_(2,2)are the weighting coefficients for the second filter (e.g., derived fromvalues selected for corresponding parameters—a_(1,2), a_(2,2), N_(1,2),N_(2,2)—of the second analyte rate-of-change estimate); and N_(1,3) andN_(2,3) are time delay indices for the third filter, and b_(0,3),b_(1,3), and b_(2,3) are the weighting coefficients for the third filter(e.g., derived from values selected for correspondingparameters—a_(1,3), a_(2,3), N_(1,3), N_(2,3)—of the third analyterate-of-change estimate). Again, it should be appreciated that whilethree filters are shown in the example embodiment, any other number offilters may be implemented in other embodiments—e.g., 2 filters, 4filters, 5 filters, etc.

The parameter values for the first, second, and third analyte pointestimates may be selected, as similarly discussed above. Again, commonaspects are not described again in detail. Furthermore, as uncompensatedanalyte measurements are received, which may include invalid and/orunavailable data intermittently, the blood glucose point estimate maythen be computed based on the output of one or more filters to generatelag-compensated point measurements, as similarly discussed above.

It should be appreciated that for embodiments where both the analytepoint estimate and the analyte rate-of-change estimate are implemented,parallel filters may be implemented for the analyte point estimateand/or the analyte rate-of-change estimate. It should be appreciatedthat in some instances, the time delay indices as well as thecoefficients of the analyte point estimate may be very different fromthat of the analyte rate-of-change estimate.

FIG. 6 illustrates a flowchart for a method of lag-compensation ofanalyte rate of change measurements with three analyte rate-of-changefilters, according to one embodiment. It should be appreciated thatsimilar methods for other number of filters (e.g., two, four, five,etc.) may be similarly implemented in other embodiments. Again, for thesake of clarity and brevity, common aspects will not be described ingreat detail again.

At blocks 605, reference analyte measurements are received. At block610, error metrics are calculated for a plurality of combinations ofvalues as parameters in an analyte rate-of-change estimate. Three setsof parameter values are then selected based on the calculated errormetrics, as represented at block 615. After selecting the sets ofparameter values, a first, second, and third set of weightingcoefficients are derived using the first, second, and third set ofparameter values, respectively, as represented by block 620. Theweighting coefficients are then implemented in three rate-of-changefilters that may be used to calculate lag-compensated rate-of-changemeasurements using the uncompensated analyte measurements (e.g.,interstitial glucose measurements), as represented by block 625. Forexample, as described earlier, the available lag-compensatedmeasurements may be averaged, may be hierarchically selected, etc.

FIG. 7 illustrates a flowchart for a method of lag-compensation ofanalyte point measurements with three analyte point filters, accordingto one embodiment. It should be appreciated that similar methods forother number of filters (e.g., two, four, five, etc.) may be similarlyimplemented in other embodiments. For the sake of clarity and brevity,common aspects will not be described in great detail again.

At blocks 705, reference analyte measurements are received. At block710, error metrics are calculated for a plurality of combinations ofvalues as parameters in an analyte point estimate. Three sets ofparameter values are then selected based on the calculated errormetrics, as represented at block 715. After selecting the sets ofparameter values, a first, second, and third set of weightingcoefficients are derived using the first, second, and third set ofparameter values, respectively, as represented by block 720. Theweighting coefficients are then implemented in three point filters thatmay be used to calculate lag-compensated point measurements using theuncompensated analyte measurements (e.g., interstitial glucosemeasurements), as represented by block 725. For example, as describedearlier, the available lag-compensated measurements may be averaged, maybe hierarchically selected, etc.

FIG. 8 illustrates a flowchart for a method of lag-compensation ofanalyte point and rate-of-change measurements with three analyte pointfilters and three analyte rate-of-change filters, according to oneembodiment. It should be appreciated that similar methods for othernumber of filters (e.g., two, four, five, etc.) may be similarlyimplemented in other embodiments. For the sake of clarity and brevity,common aspects will not be described in great detail again.

At block 805 reference analyte measurements are received. The parametervalues are selected to optimize the analyte point estimate and analyterate-of-change estimate. For example, parameter values for the analytepoint estimate and analyte rate-of-change estimate may be determined bycalculating error metrics. At block 810, error metrics are calculatedfor a plurality of combinations of values as parameters in an analytepoint estimate and analyte rate-of-change estimate. A first, second, andthird set of parameter values for the analyte point estimate and afirst, second, and third set of parameter values for the analyterate-of-change estimate are then selected based on the calculated errormetrics, as represented at block 820. Again, as previously described,the error metrics may be generated using various optimization routines(e.g., by calculating a sum-of-squared-errors, etc.). Furthermore, insome embodiments, the parameter values may be selected based on thesmallest error metric.

First, second, and third sets of weighting coefficients are then derivedbased on corresponding first, second, and third sets of parameter valuesselected for the analyte point and rate-of-change estimates, asrepresented by block 830. The sets of weighting coefficients are thenimplemented in analyte point filters and analyte rate-of-change filtersthat may each be used to calculate lag-compensated point measurementsand lag-compensated rate-of-change measurements by applying thecorresponding sets of weighting coefficients to uncompensated analytemeasurements (e.g., interstitial glucose measurements) received at thecorresponding times of each filter (e.g., the most recent time and theselected time indices (prior reference times), as represented by block835.

As uncompensated analyte measurements are received, which may includeinvalid and/or unavailable data intermittently, the lag-compensatedpoint and rate-of-change measurements may be calculated based on theoutput of one or more point and rate-of-change filters with valid datapresent, respectively. For example, in one embodiment, lag-compensatedrate-of-change measurements and lag-compensated point measurements maybe calculated, as the average of any combination of the calculations ofthe respective rate-of-change and point filters that generate a resultat any given time k. In another embodiment, lag-compensatedrate-of-change measurements and lag-compensated point measurements maybe chosen in an hierarchical order—e.g., from the respective firstrate-of-change and point filter associated with the best error metric ifvalid data is available for the first filter; from the respective secondrate-of-change and point filter associated with the second best errormetric if valid data is not available for the first filter, butavailable for the second filter; and from the respective thirdrate-of-change and point filter with third best error metric if validdata is not available for the first and second filters, but availablefor the third filter.

It should be appreciated that the preceding is exemplary, and that thelag-compensated rate-of-change measurements may be calculated from thethree parallel filters in other various combinations, averages, weightedsums, etc. Furthermore, in one embodiment, the prior reference times inthe analyte point estimate may be the same as the prior reference timesin the analyte rate-of-change estimate. In other embodiments, the priorreference times may differ. It should also be appreciated that theanalyte point filters are independent of the analyte rate-of-changefilters and may be configured differently from one another.

Example

The following is provided as an exemplary illustration, and should notbe interpreted as limiting. The glucose point estimate at any sampleinstance k is estimated by the average of any of the available filteroutputs:

Ĝ ₁(k)=1.73y(k)−0.30y(k−7)−0.46y(k−14)

Ĝ ₂(k)=1.77y(k)−0.28y(k−6)−0.53y(k−13)

Ĝ ₃(k)=1.85y(k)−0.31y(k−5)−0.57y(k−12)

Similarly, the glucose rate estimate at any sample instance k isestimated by the average of any of the available filter outputs:

Ĝ ₁(k)=0.074y(k)−0.051y(k−7)−0.023y(k−14)

Ĝ ₂(k)=0.085y(k)−0.056y(k−5)−0.029y(k−14)

Ĝ ₃(k)=0.086y(k)−0.051y(k−5)−0.035y(k−12)

Multiple Banks

Temporal sensor artifacts known as dropouts may cause the raw sensorreading to read abnormally low for a period of time, but may remain in aphysiologically valid range of glucose concentration values. In someinstances, algorithms that mitigate lag may further exacerbate thisproblem by being more sensitive to the rapid changes in blood glucosecaused by these dropouts compared to an algorithm that does not attemptto mitigate lag. In such case, the system may predict a significantlylower value during the initial phase of the dropout (negative overshoot)and a significantly higher value during the recovery phase of thedropout (positive overshoot).

FIG. 9 illustrates a graph of an example analyte measurement plot havingdropouts. Line 905 shows uncompensated glucose measurements (e.g.,received from an implanted glucose sensor) having dropouts, as indicatedat the two dips D1 and D2. The time scale is shown in hours.

Lines 910 and 915 illustrate example lag-compensated measurements andtheir corresponding dropouts at D1 and D2. As shown, the lag-correctedsignal includes negative and positive overshoot observed around theonset and recovery of the dropouts at D1 and D2. Points 920 illustratethe YSI reference glucose measurements (e.g., standard referencemeasurements from blood samples) measured approximately every 15minutes. In the graph shown, the lag correction improves sensor accuracyin general, but may degrade accuracy around dropouts. This is especiallycrucial in the hypoglycemic range.

In some aspects of the present disclosure, multiple banks areimplemented to mitigate temporal sensor artifacts, such as dropouts,invalid, physiologically infeasible, or missing data. As will bedemonstrated below, the aggressiveness of lag correction is dynamicallyadjusted based on a temporal noise metric that detects the presence oftransient glucose rates of change that is physiologically infeasible.

In some aspects of the present disclosure, a second analyterate-of-change estimate (and/or a second analyte point estimate) isprovided. The second estimate includes a time delay from the firstestimate such that the most recent sensor data for the second estimatewill be delayed from the most recent sensor data for the first estimate.

For example, as described earlier, the glucose rate-of-change estimatemay be represented as follows:

Ĝ ₁(k)=d ₀ y(k−N ₀)+d ₁ y(k−N ₁)+d ₂ y(k−N ₂)

And, the glucose point estimate may be represented as follows:

Ĝ ₁(k)=b ₀ y(k−N ₀)+b ₁ y(k−N ₁)+b ₂ y(k−N ₂)

While only a single filter for each is shown for the sake of clarity andbrevity, it should be appreciated that multiple banks may also beimplemented with multiple filters. Further, it should be appreciatedthat while the following is described in the context of an additionalbank, any number of additional banks may be implemented in variousembodiments.

In one embodiment, the second analyte rate-of-change estimate isgenerated with the latest sensor value used being a value that isdelayed M₀ steps behind (at any given time k). The following representsan example second analyte rate-of-change estimate delayed by M₀:

Ĝ _(2T)(k)=e ₀ y(k−M ₀)+e ₁ y(k−M ₁)+e ₂ y(k−M ₂)

M ₂ >M ₁ >M ₀>0

wherein k is the sample time index of the sensor data; y is thecalibrated sensor measurement; e₀, e₁, and e₂ are scalars, and M₀, M₁,and M₂ are time delay indices. While N₀ in the first estimate refers toan initial reference time (e.g., as shown equal to 0 for the most recentdata), M₀ in the second estimate refers to an alternate initialreference time that is delayed by M₀ from the initial reference time. M₁is a prior reference time that is delayed from M₀, and M₂ is a priorreference time that is delayed form M₁.

Similarly, the second analyte point estimate may be represented asfollows:

Ĝ _(2T)(k)=f ₀ y(k−M ₀)+f ₁ y(k−M ₁)+f ₂ y(k−M ₂)

M ₂ >M ₁ >M ₀>0

wherein k is the sample time index of the sensor data; y is thecalibrated sensor measurement; f₀, f₁ and f₂ are scalars, and M₀, M₁,and M₂ are time delay indices. Again, while N₀ in the first estimaterefers to an initial reference time (e.g., as shown equal to 0 for themost recent data), M₀ in the second estimate refers to an alternateinitial reference time that is delayed by M₀ from the initial referencetime. M₁ is a prior reference time that is delayed from M₀, and M₂ is aprior reference time that is delayed form M₁.

It should be appreciated that in other embodiments, the initialreference time of the first estimate may be a non-zero value (i.e.,includes an N₀ initial time delay), in which case the alternate initialtime delay of the second estimate (M₀) would be greater than thenon-zero value of the first estimate.

It should be appreciated that in some instances, each set of thereference times for the analyte point estimates (and/or analyterate-of-change estimates) is unique as a whole. In other words, two (ormore) banks may include one or more reference times in common, but theset of reference times as a whole should be as unique in that not all inone set are identical to all in another set.

Intuitively, the second estimate may not perform as well as the firstestimate on aggregate because in the majority of time, where dropoutsare nonexistent, estimating blood glucose using more recent measurementstypically yield more accurate results than using less recentmeasurements. However, provided that the delay M₀ is not extremelylarge, both the first estimate and the second estimate may producesimilar results.

When M₀ is small enough so that both the first estimate and the secondestimate generally produce similar results, but M₀ is large enough to berelatively larger than the duration of the onset and/or recovery ofdropouts, a large deviation between the first estimate and the secondestimate can be used to infer the presence of a dropout. Exampledurations of dropouts may range from 1 to 50 minutes, such as including1 to 15 minutes. Example durations of time delay (M0) may range from 1to 45 minutes, such as including 1 to 10 minutes.

The following is a description of an example embodiment that employsthis principle of transient parity between instantaneous-information anddelayed-information based estimates. While the following is illustratedfor glucose point estimates, it should be appreciated that it may besimilarly applicable to glucose rate-of-change estimates.

In one embodiment, the first glucose point estimate (e.g., the firstbank, designated as primary bank) has each of its component filterscontain an initial reference time corresponding to a short delay (e.g. 0minutes, the most recent point). This will allow the primary bank toquickly react to transient artifacts. The primary bank generates G₁, anestimate at any sample time k.

The second glucose point estimate (e.g., a second bank, designated assecondary bank) has the shortest delay in its component filters to besome period of time based on the projected size of the artifacts onsetand/or recovery (e.g. represented as N_(VART) below). The secondary bankgenerates a second estimate G_(2T), at any sample time k. At each timestep, a moving average of the difference between the outputs of thesebanks (i.e. G₁-G_(2T)) from the most recent N_(VART) minutes iscomputed. The primary bank will follow the transient artifacts in theraw glucose data, while the secondary bank will not be affected based onits delayed reaction time. This moving average difference may then bescaled by a capped average of CGM points in the recent past (i.e.present to N_(VART)−1 minutes in the past). In other words, the movingaverage difference is divided by the smaller of either a predeterminedcap, G_(ST), or the average of CGM points in the past N_(VART) minutes.Finally, a scaling factor K_(VART) may be applied when necessary to becombined with other metrics. The described variance may be representedby the following:

${\sigma_{T}^{2}(k)} = {K_{VART}\frac{\frac{1}{N_{{VART}{({VALID})}}}{\sum\limits_{j = 0}^{N_{VART}^{- 1}}\left\lbrack {{G_{1}\left( {k - j} \right)} - {G_{2T}\left( {k - j} \right)}} \right\rbrack^{2}}}{\min \left( {G_{ST},{\frac{1}{N_{{VART}{({VALID})}}}{\sum\limits_{j = 0}^{N_{VART}^{- 1}}{y\left( {k - j} \right)}}}} \right)}}$

Thus, the three parameters primarily determine the response to transientartifacts. The difference between G₁ and G_(2T) largely establishes themagnitude of the noise metric. A larger window would lead to a largermetric, as the secondary bank would remain relatively unchanged whilethe primary bank reacted to the transient. A larger averaging windowN_(VART) causes these changes to persist, leading to a noise metric thattends to remain high for a longer period of time. Finally, a scalingfactor K_(VART) determines the magnitude of the final response. Theseparameters are selected, for example, based on the magnitude andduration of the transient artifacts that are to be identified

The result is a metric σ_(T) ² that reacts quickly to fast transientartifacts and remains high for some time, based on the differences inthe minimum delays of the two banks. The metric can then be used toweight the extent of lag correction based on the presence of transientartifacts.

The following illustrates one example method of weighting the outputs.It should be appreciated that other methods may be implemented betweenbanks in other embodiments. A third bank G_(2S) is defined, in which thecalculation is made to be less sensitive to the negative effects oftransient artifacts at the expense of reduced nominal accuracy relativeto the first (and preferred) bank G₁. For example, G_(2S) can be aweighted average of the most recent N_(SLOW) sensor values, where theweights are derived from the auto-correlation function of referenceglucose data or synthesized using other methods. This does not precludesetting G_(2S) equal to G_(2T) in one embodiment.

Given the first bank, G₁, and a third bank, G_(2S), and the metric σ_(T)² to estimate the severity of a transient artifact, the blood glucoseestimate at any time k can be written as a weighted sum between the twobanks:

${{\hat{G}}_{b}(k)} = \left\{ \begin{matrix}{\left\lbrack {{w_{1}(k)}{G_{1}(k)}} \right\rbrack + \left\lbrack {{w_{2}(k)}{G_{2S}(k)}} \right\rbrack} & {{if}\mspace{14mu} {G_{1}(k)}\mspace{14mu} {and}\mspace{14mu} {G_{2S}(k)}\mspace{14mu} {are}\mspace{14mu} {available}} \\{G_{1}(k)} & {{if}\mspace{14mu} {G_{2S}(k)}\mspace{14mu} {is}\mspace{14mu} {unavailable}} \\{G_{2S}(k)} & {{if}\mspace{14mu} {G_{1}(k)}\mspace{14mu} {is}\mspace{14mu} {unavailable}}\end{matrix} \right.$

wherein the weight for the first term is made to approach 0 when σ_(T) ²is large, and made to approach 1 when σ_(T) ² is small. One example isto use a baseline value σ_(To) ² computed a priori, whose value istypically in the same order as σ_(T) ² when no transient artifactoccurs. The normalization can then be scaled by a power P, which can beused to adjust the sensitivity of the weight w₁. For example, setting Pat a higher value (e.g. 4 instead of 1) makes the weight w₁ drop fasterto 0 when transients occur.

${W_{1}(k)} = \frac{1}{1 + \left\lbrack \frac{\sigma_{T}^{2}(k)}{\sigma_{To}^{2}} \right\rbrack^{F}}$

wherein the weight for the second term is such that the sum of theweights equal to 1: w₁+w₂=1. It should be understood that variations canbe made to consider recent past availability of G₁ and G_(2S) inaddition to their latest availability.

FIG. 10 illustrates a flowchart for a method of lag compensation ofanalyte rate-of-change measurements with multiple banks, according toone embodiment. The method in FIG. 10 may be implemented, for example,with respect to the method shown in FIG. 3, and reference to the methodin FIG. 3 is made. It should be appreciated, that the principles may besimilarly and equally applicable to FIG. 6.

At block 1010, additional sets of parameter values are determined foradditional analyte rate-of-change estimates. The additional analyterate-of-change estimates are based on sums of a plurality of scaledrates-of-changes. The plurality of scaled rates-of-changes for each ofthe additional analyte rate-of-change estimates are from an alternateinitial reference time to prior reference times with respect to thealternate initial reference time. The alternate initial reference timeis prior to the initial reference time by a time delay. For instance,the time delay may be predetermined based on a projected size ofartifacts.

For example, referring back to FIG. 3, if a second bank was implemented,another set of parameter values for a second analyte rate-of-changeestimate is determined. In the second estimate, however, therates-of-changes are from an alternate initial reference time to twodifferent prior reference times respectively. The alternate initialreference time is selected such that it is prior to the initialreference time in the first bank, and thus is delayed from the firstbank.

At block 1015, a set of weighting coefficients is derived from the setof parameter values of the first bank (e.g., from FIG. 3). At block1020, lag-compensated rate-of-change measurements are calculated for thefirst bank. For example, first lag-compensated rate-of-changemeasurements are calculated from the uncompensated analyte measurementsby applying the set of weighting coefficients of the first bank tocorresponding uncompensated analyte measurements received at the initialreference time and prior reference times of the set of parameter valuesof the first bank (e.g., from FIG. 3).

At block 1025, additional sets of weighting coefficients are derivedfrom the additional sets of parameter values. At block 1030,lag-compensated rate-of-change measurements are calculated for theadditional banks. For example, additional lag-compensated rate-of-changemeasurements are calculated from the uncompensated analyte measurementsby applying the additional sets of weighting coefficients tocorresponding uncompensated analyte measurements received at thealternate initial reference time and at the prior reference times ofeach of the additional sets of parameter values.

For example, referring back to the example of FIG. 3 and the secondbank, a second set of weighting coefficients are derived from theparameter values selected for the second analyte rate-of-changeestimate. These weighting coefficients are accordingly applied to theuncompensated analyte measurements that are received at the alternateinitial reference time and at the prior reference times of the secondbank, in order to calculate lag-compensated rate-of-change measurementsassociated with the second bank.

At block 1035, a severity of discrepancy is determined between theanalyte rate-of-change estimates. At block 1040, resultinglag-compensated rate-of-change measurements are calculated based on aweighted sum of the lag compensated output of the first bank and theadditional lag-compensated outputs of the additional banks. The weightedsum is based on the severity of discrepancy.

For example, in the example of FIG. 3 and the second bank, a severity ofdiscrepancy between the two lag-compensated rate-of-change measurementsfor the first and second bank is determined and used to calculateresulting lag-compensated rate-of-change measurements. For example, asdescribed above, a weighted sum of the two outputs may be based on theseverity of discrepancy.

FIG. 11 illustrates a flowchart for a method of lag compensation ofanalyte point measurements with multiple banks, according to oneembodiment. The method in FIG. 11 may be implemented, for example, withrespect to the method shown in FIG. 4, and reference is made to FIG. 4.It should be appreciated, that the principles may be similarly andequally applicable to FIG. 7.

At block 1105, additional sets of parameter values for one or moreadditional analyte point estimates are determined. The additionalanalyte point estimates are based on sums of an analyte point and sumsof a plurality of scaled rates-of-changes. The analyte point correspondsto measurements at an alternate initial reference time that is prior tothe initial reference time of the first analyte point estimate for thefirst bank by a time delay. For example, the time delay may bepredetermined based on an assumed distribution of duration and size ofartifacts. The plurality of scaled rates-of-changes for each of theadditional analyte point estimates are from the alternate initialreference time to various prior reference times with respect to thealternate initial reference time.

For example, referring back to FIG. 4 and the second bank, another setof parameter values for a second analyte point estimate is determined.The second estimate, however, includes an analyte point corresponding toan alternate initial reference time that is prior the initial referencetime by a time delay. In this way, the second bank is delayed from thefirst bank. Furthermore, the rates-of-changes for the second estimateare from an alternate initial reference time to two different priorreference times respectively. The alternate initial reference time isselected such that it is prior to the initial reference time in thefirst bank, and thus is delayed from the first bank.

At block 1110, a set of weighting coefficients is derived from the setof parameter values of the first bank (e.g., from FIG. 4). At block1115, lag-compensated point measurements for the first bank arecalculated. For example, lag-compensated point measurements arecalculated from the uncompensated analyte measurements by applying theset of weighting coefficients of the first bank to correspondinguncompensated analyte measurements received at the initial referencetime and the prior reference times of the set of parameter values of thefirst bank (e.g., from FIG. 4).

At block 1120, additional sets of weighting coefficients are derivedfrom the additional sets of parameter values. At block 1125, additionallag-compensated point measurements for the additional banks arecalculated. For example, additional lag-compensated point measurementsare calculated from the uncompensated analyte measurements by applyingthe additional sets of weighting coefficients of the additional banks tocorresponding uncompensated analyte measurements received at thealternate initial reference time and at the prior reference times ofeach of the additional sets of parameter values.

For example, referring back to the example of FIG. 4 and the secondbank, a set of weighting coefficients for the second bank are derivedfrom the parameter values selected for the second analyte pointestimate. These weighting coefficients are accordingly applied to theuncompensated analyte measurements that are received at the alternateinitial reference time and at the prior reference times of the secondbank, in order to calculate lag-compensated point measurementsassociated with the second bank.

At block 1130, a severity of discrepancy is determined between theanalyte point estimates. At block 1135, resulting lag-compensated pointmeasurements are calculated based on a weighted sum of the lagcompensated point measurements of the first bank and the additionallag-compensated point measurements of the additional banks. The weightedsum is based on the severity of discrepancy.

For example, in the example of FIG. 4 and the second bank, a severity ofdiscrepancy between the two lag-compensated point measurements for thefirst and second bank is determined and used to calculate resultinglag-compensated point measurements. For example, as described above, aweighted sum of the two outputs may be based on the severity ofdiscrepancy.

Furthermore, it should be appreciated that some embodiments may includeadditional banks in both analyte point and rate-of-change estimates.FIG. 12, for example, illustrates a flowchart for a method of lagcompensation of analyte point and rate-of-change measurements withmultiple banks in each, according to one embodiment. The method in FIG.12 may be implemented, for example, with respect to the method shown inFIG. 5, and reference is made to FIG. 5. It should be appreciated, thatthe principles may be similarly and equally applicable to FIG. 8. It isnoted that the methods shown in FIGS. 10 and 11 above, may beindependently performed to provide respective outputs. For the sake ofclarity and brevity, common aspects will not be described in greatdetail again.

At block 1205, additional sets of parameter values for additionalanalyte point and rate-of-change estimates (e.g., additional banks) aredetermined. At block 1210, sets of weighting coefficients for the firstbank are derived from the first set of parameter values for the analytepoint and rate-of-change estimates of the first bank (e.g., from FIG.5).

At block 1215, lag-compensated point measurements are calculated fromthe uncompensated analyte measurements by applying the first sets ofweighting coefficients for the analyte point and rate-of-changeestimates of the first bank to corresponding uncompensated analytemeasurements received at the initial reference time and the priorreference times of the first set of parameter values for the analytepoint and rate-of-change estimates of the first bank (e.g., from FIG.5).

At block 1220, additional sets of weighting coefficients are derivedfrom the additional sets of parameter values for the analyte point andrate-of-change estimates of the additional banks. At block 1225,additional lag-compensated point measurements are calculated for theadditional banks. For example, additional lag-compensated pointmeasurements are calculated from the uncompensated analyte measurementsby applying the additional sets of weighting coefficients for the firstbank to corresponding uncompensated analyte measurements received at thealternate initial reference time and at the prior reference times ofeach of the additional sets of parameter values for the analyte pointand rate-of-change estimates of the additional banks

At block 1230, a severity of discrepancy is determined between theanalyte point estimates, and between the analyte rate-of-changeestimates. At block 1235, resulting lag-compensated point measurementsare generated based on a weighted sum of the lag compensated pointmeasurements of the first bank and the additional lag-compensated pointmeasurements for the analyte point and rate-of-change estimates of theadditional banks. The weighted sum is based on the severity ofdiscrepancy.

Devices and Systems

Embodiments of the present disclosure relate to the continuous,periodic, and/or on demand in vivo monitoring of the level of one ormore analytes using a continuous, periodic, intermittent, or on-demandanalyte monitoring device or system. The system may include an analytesensor at least a portion of which is to be positioned beneath a skinsurface of a user for a period of time. Systems may include whollyimplantable analyte sensors and analyte sensors in which only a portionof the sensor is positioned under the skin and a portion of the sensorresides above the skin, e.g., for contact to a sensor control unit(which may include a transmitter), a receiver/display unit, transceiver,processor, etc. The sensor may be, for example, subcutaneouslypositionable in a user for the continuous, periodic, or on-demandmonitoring of a level of an analyte in the user's interstitial fluid.

An analyte sensor may be positioned in contact with interstitial fluidto detect the level of glucose, which detected glucose may be used toinfer the glucose level in the user's bloodstream. Embodiments of theanalyte sensors may be configured for monitoring the level of theanalyte over a time period which may range from seconds, minutes, hours,days, weeks, to months, or longer. In one embodiment, the analytesensors, such as glucose sensors, are capable of in vivo detection of ananalyte for one hour or more, e.g., a few hours or more, e.g., a fewdays or more, e.g., three or more days, e.g., five days or more, e.g.,seven days or more, e.g., several weeks or more, or one month or more.

As demonstrated herein, the methods of the present disclosure are usefulin connection with a device that is used to measure or monitor ananalyte (e.g., glucose), such as any such device described herein. Thesemethods may also be used in connection with a device that is used tomeasure or monitor another analyte (e.g., ketones, ketone bodies, HbAlc,and the like), including oxygen, carbon dioxide, proteins, drugs, oranother moiety of interest, for example, or any combination thereof,found in bodily fluid, including subcutaneous fluid, dermal fluid(sweat, tears, and the like), interstitial fluid, or other bodily fluidof interest, for example, or any combination thereof.

FIG. 13 shows an analyte (e.g., glucose) monitoring system, according toone embodiment. Aspects of the subject disclosure are further describedprimarily with respect to glucose monitoring devices and systems, andmethods of glucose detection, for convenience only and such descriptionis in no way intended to limit the scope of the embodiments. It is to beunderstood that the analyte monitoring system may be configured tomonitor a variety of analytes at the same time or at different times.

Analytes that may be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin,glycosylated hemoglobin (HbAlc), creatine kinase (e.g., CK-MB),creatine, creatinine, DNA, fructosamine, glucose, glucose derivatives,glutamine, growth hormones, hormones, ketones, ketone bodies, lactate,peroxide, prostate-specific antigen, prothrombin, RNA, thyroidstimulating hormone, and troponin. The concentration of drugs, such as,for example, antibiotics (e.g., gentamicin, vancomycin, and the like),digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may alsobe monitored. In embodiments that monitor more than one analyte, theanalytes may be monitored at the same or different times.

The analyte monitoring system 1400 includes an analyte sensor 1401, adata processing unit 1402 connectable to the sensor 1401, and a primaryreceiver unit 1404. In some instances, the primary receiver unit 1404 isconfigured to communicate with the data processing unit 1402 via acommunication link 1403. In one embodiment, the primary receiver unit1404 may be further configured to transmit data to a data processingterminal 1405 to evaluate or otherwise process or format data receivedby the primary receiver unit 1404. The data processing terminal 1405 maybe configured to receive data directly from the data processing unit1402 via a communication link 1407, which may optionally be configuredfor bi-directional communication. Further, the data processing unit 1402may include a transmitter or a transceiver to transmit and/or receivedata to and/or from the primary receiver unit 1404 and/or the dataprocessing terminal 1405 and/or optionally a secondary receiver unit1406.

Also shown in FIG. 13 is an optional secondary receiver unit 1406 whichis operatively coupled to the communication link 1403 and configured toreceive data transmitted from the data processing unit 1402. Thesecondary receiver unit 1406 may be configured to communicate with theprimary receiver unit 1404, as well as the data processing terminal1405. In one embodiment, the secondary receiver unit 1406 may beconfigured for bi-directional wireless communication with each of theprimary receiver unit 1404 and the data processing terminal 1405. Asdiscussed in further detail below, in some instances, the secondaryreceiver unit 1406 may be a de-featured receiver as compared to theprimary receiver unit 1404, for instance, the secondary receiver unit1406 may include a limited or minimal number of functions and featuresas compared with the primary receiver unit 1404. As such, the secondaryreceiver unit 1406 may include a smaller (in one or more, including all,dimensions), compact housing or embodied in a device including a wristwatch, arm band, PDA, mp3 player, cell phone, etc., for example.Alternatively, the secondary receiver unit 106 may be configured withthe same or substantially similar functions and features as the primaryreceiver unit 1404. The secondary receiver unit 106 may include adocking portion configured to mate with a docking cradle unit forplacement by, e.g., the bedside for night time monitoring, and/or abi-directional communication device. A docking cradle may recharge apower supply.

Only one analyte sensor 1401, data processing unit 1402 and dataprocessing terminal 1405 are shown in the embodiment of the analytemonitoring system 1400 illustrated in FIG. 13. However, it will beappreciated by one of ordinary skill in the art that the analytemonitoring system 1400 may include more than one sensor 1401 and/or morethan one data processing unit 1402, and/or more than one data processingterminal 1405. Multiple sensors may be positioned in a user for analytemonitoring at the same or different times.

The analyte monitoring system 1400 may be a continuous monitoringsystem, or semi-continuous, or a discrete monitoring system. In amulti-component environment, each component may be configured to beuniquely identified by one or more of the other components in the systemso that communication conflict may be readily resolved between thevarious components within the analyte monitoring system 1400. Forexample, unique IDs, communication channels, and the like, may be used.

In one embodiment, the sensor 1401 is physically positioned in or on thebody of a user whose analyte level is being monitored. The sensor 1401may be configured to at least periodically sample the analyte level ofthe user and convert the sampled analyte level into a correspondingsignal for transmission by the data processing unit 1402. The dataprocessing unit 1402 is coupleable to the sensor 1401 so that bothdevices are positioned in or on the user's body, with at least a portionof the analyte sensor 1401 positioned transcutaneously. The dataprocessing unit may include a fixation element, such as an adhesive orthe like, to secure it to the user's body. A mount (not shown)attachable to the user and mateable with the data processing unit 1402may be used. For example, a mount may include an adhesive surface. Thedata processing unit 1402 performs data processing functions, where suchfunctions may include, but are not limited to, filtering and encoding ofdata signals, each of which corresponds to a sampled analyte level ofthe user, for transmission to the primary receiver unit 1404 via thecommunication link 1403. In one embodiment, the sensor 1401 or the dataprocessing unit 1402 or a combined sensor/data processing unit may bewholly implantable under the skin surface of the user.

In one embodiment, the primary receiver unit 1404 may include an analoginterface section including an RF receiver and an antenna that isconfigured to communicate with the data processing unit 1402 via thecommunication link 1403, and a data processing section for processingthe received data from the data processing unit 1402 including datadecoding, error detection and correction, data clock generation, databit recovery, etc., or any combination thereof.

The primary receiver unit 1404 in one embodiment is configured tosynchronize with the data processing unit 1402 to uniquely identify thedata processing unit 1402, based on, for example, an identificationinformation of the data processing unit 1402, and thereafter, toperiodically receive signals transmitted from the data processing unit1402 associated with the monitored analyte levels detected by the sensor1401.

The data processing terminal 1405 may include a personal computer, aportable computer including a laptop or a handheld device (e.g., apersonal digital assistant (PDA), a telephone including a cellular phone(e.g., a multimedia and Internet-enabled mobile phone including aniPhone™, a Blackberry®, or similar phone), an mp3 player (e.g., aniPOD™, etc.), a pager, and the like), and/or a drug delivery device(e.g., an infusion device), each of which may be configured for datacommunication with the receiver via a wired or a wireless connection.Additionally, the data processing terminal 1405 may further be connectedto a data network (not shown) for storing, retrieving, updating, and/oranalyzing data corresponding to the detected analyte level of the user.

The data processing terminal 1405 may include a drug delivery device(e.g., an infusion device) such as an insulin infusion pump or the like,which may be configured to administer a drug (e.g., insulin) to theuser, and which may be configured to communicate with the primaryreceiver unit 104 for receiving, among others, the measured analytelevel. Alternatively, the primary receiver unit 1404 may be configuredto integrate an infusion device therein so that the primary receiverunit 1404 is configured to administer an appropriate drug (e.g.,insulin) to users, for example, for administering and modifying basalprofiles, as well as for determining appropriate boluses foradministration based on, among others, the detected analyte levelsreceived from the data processing unit 1402. An infusion device may bean external device or an internal device, such as a device whollyimplantable in a user.

In one embodiment, the data processing terminal 1405, which may includean infusion device, e.g., an insulin pump, may be configured to receivethe analyte signals from the data processing unit 1402, and thus,incorporate the functions of the primary receiver unit 1404 includingdata processing for managing the user's insulin therapy and analytemonitoring. In one embodiment, the communication link 1403, as well asone or more of the other communication interfaces shown in FIG. 13, mayuse one or more wireless communication protocols, such as, but notlimited to: an RF communication protocol, an infrared communicationprotocol, a Bluetooth enabled communication protocol, an 802.11xwireless communication protocol, or an equivalent wireless communicationprotocol which would allow secure, wireless communication of severalunits (for example, per Health Insurance Portability and AccountabilityAct (HIPPA) requirements), while avoiding potential data collision andinterference.

FIG. 14 is a block diagram of the data processing unit 1402 shown inFIG. 13 in accordance with one embodiment. Data processing unit 1402includes an analog interface 1501 configured to communicate with thesensor 1401, a user input 1502, and a temperature measurement section1503, each of which is operatively coupled to processor 1504 such as acentral processing unit (CPU). Furthermore, unit 1402 is shown toinclude a serial communication section 1505, clock 1508, and an RFtransmitter 1506, each of which is also operatively coupled to theprocessor 1504. Moreover, a power supply 1507 such as a battery is alsoprovided in unit 1402 to provide the necessary power.

It should be appreciated that in another embodiment, the data processingunit may not include all components in the exemplary embodiment shown.User input and/or interface components may be included or a dataprocessing unit may be free of user input and/or interface components.In one embodiment, one or more application-specific integrated circuits(ASIC) may be used to implement one or more functions or routinesassociated with the operations of the data processing unit (and/orreceiver unit) using for example one or more state machines and buffers.

The analyte sensor 1401 is shown including four contacts, three of whichare electrodes: a work electrode (W) 1510, a reference electrode (R)1512, and a counter electrode (C) 1513, each operatively coupled to theanalog interface 1501 of the data processing unit 1402. This embodimentalso shows an optional guard contact (G) 1511. Fewer or greaterelectrodes may be employed. For example, the counter and referenceelectrode functions may be served by a single counter/referenceelectrode. In some cases, there may be more than one working electrodeand/or reference electrode and/or counter electrode, etc.

FIG. 15 is a block diagram of an embodiment of a receiver/monitor unitsuch as the primary receiver unit 1404 of the analyte monitoring systemshown in FIG. 13. The primary receiver unit 1404 includes one or moreof: a test strip interface 1601, an RF receiver 1602, a user input 1603,an optional temperature detection section 1604, and a clock 1605, eachof which is operatively coupled to a processing and storage section1607. The primary receiver unit 1404 also includes a power supply 1606operatively coupled to a power conversion and monitoring section 1608.Further, the power conversion and monitoring section 1608 is alsocoupled to the processing and storage section 1607. Moreover, also shownare a receiver serial communication section 1609, and an output 1610,each operatively coupled to the processing and storage section 1607. Theprimary receiver unit 1404 may include user input and/or interfacecomponents or may be free of user input and/or interface components.

In one embodiment, the test strip interface 1601 includes an analytetesting portion (e.g., a glucose level testing portion) to receive ablood (or other body fluid sample) analyte test or information relatedthereto. For example, the test strip interface 1601 may include a teststrip port to receive a test strip (e.g., a glucose test strip). Thedevice may determine the analyte level of the test strip, and optionallydisplay (or otherwise notice) the analyte level on the output 1610 ofthe primary receiver unit 1404. Any suitable test strip may be employed,e.g., test strips that only require a very small amount (e.g., 3microliters or less, e.g., 1 microliter or less, e.g., 0.5 microlitersor less, e.g., 0.1 microliters or less), of applied sample to the stripin order to obtain accurate glucose information. Embodiments of teststrips include, e.g., Freestyle® blood glucose test strips from AbbottDiabetes Care, Inc. (Alameda, Calif.). Glucose information obtained byan in vitro glucose testing device may be used for a variety ofpurposes, computations, etc. For example, the information may be used tocalibrate sensor 1401, confirm results of sensor 1401 to increase theconfidence thereof (e.g., in instances in which information obtained bysensor 1401 is employed in therapy related decisions), etc.

In further embodiments, the data processing unit 1402 and/or the primaryreceiver unit 1404 and/or the secondary receiver unit 1406, and/or thedata processing terminal/infusion device 1405 may be configured toreceive the analyte value wirelessly over a communication link from, forexample, a blood glucose meter. In further embodiments, a usermanipulating or using the analyte monitoring system 1400 (FIG. 13) maymanually input the analyte value using, for example, a user interface(for example, a keyboard, keypad, voice commands, and the like)incorporated in one or more of the data processing unit 1402, theprimary receiver unit 1404, secondary receiver unit 1406, or the dataprocessing terminal/infusion device 1405.

Additional detailed descriptions are provided in U.S. Pat. Nos.5,262,035; 5,264,104; 5,262,305; 5,320,715; 5,593,852; 6,175,752;6,650,471; 6,746, 582, and 7,811,231, each of which is incorporatedherein by reference in their entirety.

In certain embodiments, the sensing elements include one or moreelectron transfer agents. Electron transfer agents that may be employedare electroreducible and electrooxidizable ions or molecules havingredox potentials that are a few hundred millivolts above or below theredox potential of the standard calomel electrode (SCE). The electrontransfer agent may be organic, organometallic, or inorganic. Examples oforganic redox species are quinones and species that in their oxidizedstate have quinoid structures, such as Nile blue and indophenol.Examples of organometallic redox species are metallocenes includingferrocene. Examples of inorganic redox species are hexacyanoferrate(III), ruthenium hexamine, etc.

In certain embodiments, electron transfer agents have structures orcharges which prevent or substantially reduce the diffusional loss(e.g., non-leachable) of the electron transfer agent during the periodof time that the sample is being analyzed. For example, electrontransfer agents include but are not limited to a redox species, e.g.,bound to a polymer which can in turn be disposed on or near the workingelectrode. The bond between the redox species and the polymer may becovalent, coordinative, or ionic. Although any organic, organometallicor inorganic redox species may be bound to a polymer and used as anelectron transfer agent, in certain embodiments the redox species is atransition metal compound or complex, e.g., osmium, ruthenium, iron, andcobalt compounds or complexes. It will be recognized that many redoxspecies described for use with a polymeric component may also be used,without a polymeric component.

Embodiments of polymeric electron transfer agents may contain a redoxspecies covalently bound in a polymeric composition. An example of thistype of mediator is poly(vinylferrocene). Another type of electrontransfer agent contains an ionically-bound redox species. This type ofmediator may include a charged polymer coupled to an oppositely chargedredox species. Examples of this type of mediator include a negativelycharged polymer coupled to a positively charged redox species such as anosmium or ruthenium polypyridyl cation. Another example of anionically-bound mediator is a positively charged polymer includingquaternized poly(4-vinyl pyridine) or poly(l-vinyl imidazole) coupled toa negatively charged redox species such as ferricyanide or ferrocyanide.In other embodiments, electron transfer agents include a redox speciescoordinatively bound to a polymer. For example, the mediator may beformed by coordination of an osmium or cobalt 2,2′-bipyridyl complex topoly(l-vinyl imidazole) or poly(4-vinyl pyridine).

Suitable electron transfer agents are osmium transition metal complexeswith one or more ligands, each ligand having a nitrogen-containingheterocycle such as 2,2′-bipyridine, 1,10-phenanthroline, 1-methyl,2-pyridyl biimidazole, or derivatives thereof. The electron transferagents may also have one or more ligands covalently bound in a polymer,each ligand having at least one nitrogen-containing heterocycle, such aspyridine, imidazole, or derivatives thereof. One example of an electrontransfer agent includes (a) a polymer or copolymer having pyridine orimidazole functional groups and (b) osmium cations complexed with twoligands, each ligand containing 2,2′-bipyridine, 1,10-phenanthroline, orderivatives thereof, the two ligands not necessarily being the same.Some derivatives of 2,2′-bipyridine for complexation with the osmiumcation include but are not limited to 4,4′-dimethyl-2,2′-bipyridine andmono-, di-, and polyalkoxy-2,2′-bipyridines, including4,4′-dimethoxy-2,2′-bipyridine. Derivatives of 1,10-phenanthroline forcomplexation with the osmium cation include but are not limited to4,7-dimethyl-1,10-phenanthroline and mono, di-, andpolyalkoxy-1,10-phenanthrolines, such as4,7-dimethoxy-1,10-phenanthroline. Polymers for complexation with theosmium cation include but are not limited to polymers and copolymers ofpoly(l-vinyl imidazole) (referred to as “PVI”) and poly(4-vinylpyridine) (referred to as “PVP”). Suitable copolymer substituents ofpoly(l-vinyl imidazole) include acrylonitrile, acrylamide, andsubstituted or quaternized N-vinyl imidazole, e.g., electron transferagents with osmium complexed to a polymer or copolymer of poly(l-vinylimidazole).

Embodiments may employ electron transfer agents having a redox potentialranging from about −200 mV to about +200 mV versus the standard calomelelectrode (SCE). The sensing elements may also include a catalyst whichis capable of catalyzing a reaction of the analyte. The catalyst mayalso, in some embodiments, act as an electron transfer agent. Oneexample of a suitable catalyst is an enzyme which catalyzes a reactionof the analyte. For example, a catalyst, including a glucose oxidase,glucose dehydrogenase (e.g., pyrroloquinoline quinone (PQQ), dependentglucose dehydrogenase, flavine adenine dinucleotide (FAD) dependentglucose dehydrogenase, or nicotinamide adenine dinucleotide (NAD)dependent glucose dehydrogenase), may be used when the analyte ofinterest is glucose. A lactate oxidase or lactate dehydrogenase may beused when the analyte of interest is lactate. Laccase may be used whenthe analyte of interest is oxygen or when oxygen is generated orconsumed in response to a reaction of the analyte.

In certain embodiments, a catalyst may be attached to a polymer, crosslinking the catalyst with another electron transfer agent, which, asdescribed above, may be polymeric. A second catalyst may also be used incertain embodiments. This second catalyst may be used to catalyze areaction of a product compound resulting from the catalyzed reaction ofthe analyte. The second catalyst may operate with an electron transferagent to electrolyze the product compound to generate a signal at theworking electrode. Alternatively, a second catalyst may be provided inan interferent-eliminating layer to catalyze reactions that removeinterferents.

In certain embodiments, the sensor works at a low oxidizing potential,e.g., a potential of about +40 mV vs. Ag/AgCl. This sensing elementsuse, for example, an osmium (Os)-based mediator constructed for lowpotential operation. Accordingly, in certain embodiments the sensingelements are redox active components that include: (1) osmium-basedmediator molecules that include (bidente) ligands, and (2) glucoseoxidase enzyme molecules. These two constituents are combined togetherin the sensing elements of the sensor.

A mass transport limiting layer (not shown), e.g., an analyte fluxmodulating layer, may be included with the sensor to act as adiffusion-limiting barrier to reduce the rate of mass transport of theanalyte, for example, glucose or lactate, into the region around theworking electrodes. The mass transport limiting layers are useful inlimiting the flux of an analyte to a working electrode in anelectrochemical sensor so that the sensor is linearly responsive over alarge range of analyte concentrations and is easily calibrated. Masstransport limiting layers may include polymers and may be biocompatible.A mass transport limiting layer may provide many functions, e.g.,biocompatibility and/or interferent-eliminating functions, etc. Incertain embodiments, a mass transport limiting layer is a membranecomposed of crosslinked polymers containing heterocyclic nitrogengroups, such as polymers of polyvinylpyridine and polyvinylimidazole.Embodiments also include membranes that are made of a polyurethane, orpolyether urethane, or chemically related material, or membranes thatare made of silicone, and the like.

In some instances, the analyte monitoring device includes processingcircuitry that is able to determine a level of the analyte and activatean alarm system if the analyte level exceeds a threshold. The analytemonitoring device, in these embodiments, has an alarm system and mayalso include a display, such as an LCD or LED display. An alarm may alsobe activated if the sensor readings indicate a value that is beyond ameasurement range of the sensor. For glucose, the physiologicallyrelevant measurement range is typically 30-400 mg/dL, including 40-300mg/dL and 50-250 mg/dL, of glucose in the interstitial fluid. The alarmsystem may also, or alternatively, be activated when the rate-of-changeor acceleration of the rate-of-change in analyte level increase ordecrease reaches or exceeds a threshold rate or acceleration. Forexample, in the case of a subcutaneous glucose monitor, the alarm systemmight be activated if the rate-of-change in glucose concentrationexceeds a threshold value which might indicate that a hyperglycemic orhypoglycemic condition is likely to occur. A system may also includesystem alarms that notify a user of system information such as batterycondition, calibration, sensor dislodgment, sensor malfunction, etc.Alarms may be, for example, auditory and/or visual. Othersensory-stimulating alarm systems may be used including alarm systemswhich heat, cool, vibrate, or produce a mild electrical shock whenactivated.

Drug Delivery System

The present disclosure may also relate to sensors used in sensor-baseddrug delivery systems. The system may provide a drug to counteract thehigh or low level of the analyte in response to the signals from one ormore sensors. Alternatively, the system may monitor the drugconcentration to ensure that the drug remains within a desiredtherapeutic range. The drug delivery system may include one or more(e.g., two or more) sensors, a processing unit such as a transmitter, areceiver/display unit, and a drug administration system. In some cases,some or all components may be integrated in a single unit. Asensor-based drug delivery system may use data from the one or moresensors to provide necessary input for a control algorithm/mechanism toadjust the administration of drugs, e.g., automatically orsemi-automatically. As an example, a glucose sensor may be used tocontrol and adjust the administration of insulin from an external orimplanted insulin pump.

Each of the various references, presentations, publications, provisionaland/or non-provisional U.S. patent applications, U.S. patents, non-U.S.Patent applications, and/or non-U.S. patents that have been identifiedherein, is incorporated herein by reference in its entirety.

Other embodiments and modifications within the scope of the presentdisclosure will be apparent to those skilled in the relevant art.Various modifications, processes, as well as numerous structures towhich the embodiments of the present disclosure may be applicable willbe readily apparent to those of skill in the art to which the presentdisclosure is directed upon review of the specification. Various aspectsand features of the present disclosure may have been explained ordescribed in relation to understandings, beliefs, theories, underlyingassumptions, and/or working or prophetic examples, although it will beunderstood that the present disclosure is not bound to any particularunderstanding, belief, theory, underlying assumption, and/or working orprophetic example. Although various aspects and features of the presentdisclosure may have been described largely with respect to applications,or more specifically, medical applications, involving diabetic humans,it will be understood that such aspects and features also relate to anyof a variety of applications involving non-diabetic humans and any andall other animals. Further, although various aspects and features of thepresent disclosure may have been described largely with respect toapplications involving partially implanted sensors, such astranscutaneous or subcutaneous sensors, it will be understood that suchaspects and features also relate to any of a variety of sensors that aresuitable for use in connection with the body of an animal or a human,such as those suitable for use as fully implanted in the body of ananimal or a human. Finally, although the various aspects and features ofthe present disclosure have been described with respect to variousembodiments and specific examples herein, all of which may be made orcarried out conventionally, it will be understood that the invention isentitled to protection within the full scope of the appended claims.

Additional Example Embodiments

As stated above, in some aspects of the present disclosure, methods oflag compensation for analyte point measurements are provided. Themethods include receiving reference analyte measurements; anddetermining a first set of parameter values for an analyte pointestimate based on the reference analyte measurements. The analyte pointestimate is based on a sum of an analyte point and a sum of a pluralityof scaled rates-of-changes. The analyte point corresponds tomeasurements at an initial reference time. The rates-of-changes includea first rate-of-change from the initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time.

In one embodiment, the first set of parameter values includes a firstscalar of the first rate-of-change, the first prior reference time, asecond scalar of the second rate-of-change, and the second priorreference time.

In one embodiment, the determining of the first set of parameter valuesincludes calculating error metrics for a plurality of combinations ofvalues as parameters in the analyte point estimate, and selecting thefirst set of parameter values based on the calculated error metrics. Insome instances, the error metrics are generated by calculating asum-of-squared-errors. In some instances, the first set of parametervalues is associated with a smallest error metric calculated.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, and calculatinglag-compensated point measurements from the uncompensated analytemeasurements. The lag-compensated point measurements are calculated byapplying the first set of weighting coefficients to correspondinguncompensated analyte measurements received at the initial referencetime, at the first prior reference time, and at the second priorreference time of the first parameter values.

In some embodiments, a second filter is implemented. In one embodiment,the method includes determining a second set of parameter values for theanalyte point estimate based on the reference analyte measurements. Thefirst set and the second set of parameter values point to different setsof prior reference times. In one embodiment, the determining of thefirst set and the second set of parameter values includes calculatingerror metrics for a plurality of combinations of values as parameters inthe analyte point estimate, and selecting the first set and the secondset of parameter values based on the calculated error metrics. In someinstances, the error metrics are generated by calculating asum-of-squared-errors.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,and calculating lag-compensated point measurements by averaging a firstoutput and a second output. The first output is generated by applyingthe first set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, and at the second prior reference time ofthe first set of parameter values. The second output is generated byapplying the second set of weighting coefficients to correspondinguncompensated analyte measurements received at the initial referencetime, at the first prior reference time, and at the second priorreference time of the second set of parameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, and deriving asecond set of weighting coefficients from the second set of parametervalues. When valid data is present at the first prior reference time andthe second prior reference time for the first set of parameter values,lag-compensated point measurements are calculated from the uncompensatedanalyte measurements by applying the first set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the first set of parameter values. Whenvalid data is present at the first prior reference time and the secondprior reference time for the second set of parameter values and not thefirst set of parameter values, lag-compensated point measurements arecalculated from the uncompensated analyte measurements by applying thesecond set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, and at the second prior reference time ofthe second set of parameter values.

In some embodiments, a third filter is implemented. In one embodiment,the method includes determining a third set of parameter values for theanalyte point estimate based on the reference analyte measurements. Thefirst, second, and third sets of parameter values point to differentsets of prior reference times. In one embodiment, the determining of thefirst set, the second set, and the third set of parameter valuesincludes calculating error metrics for a plurality of combinations ofvalues as parameters in the analyte point estimate, and selecting thefirst set, the second set, and the third set of parameter values basedon the calculated error metrics. In some instances, the error metricsare generated by calculating a sum-of-squared-errors. The first set ofparameter values is associated with a smaller calculated error metricthan the second set of parameter values, and the second set of parametervalues is associated with a smaller calculated error metric than thethird set of parameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,deriving a third set of weighting coefficients from the third set ofparameter values, and calculating lag-compensated point measurements byaveraging available outputs. The available outputs are generated byapplying the first, second, and/or third sets of weighting coefficientsto uncompensated analyte measurements received at the initial referencetime and prior reference times of respective first, second, and/or thirdsets of parameter values which have valid data present.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,and deriving a third set of weighting coefficients from the third set ofparameter values. When valid data is present at the first priorreference time and the second prior reference time for the first set ofparameter values, lag-compensated point measurements are calculated fromthe uncompensated analyte measurements by applying the first set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time for the first setof parameter values. When valid data is present at the first priorreference time and the second prior reference time of the second set ofparameter values and not the first set of parameter values,lag-compensated point measurements are calculated from the uncompensatedanalyte measurements by applying the second set of weightingcoefficients to corresponding uncompensated analyte measurementsreceived at the initial reference time, at the first prior referencetime, and at the second prior reference time of the second set ofparameter values. When valid data is present at the first priorreference time and the second prior reference time of the third set ofparameter values and not the first set or the second set of parametervalues, lag-compensated point measurements are calculated from theuncompensated analyte measurements by applying the third set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the third setof parameter values.

In some embodiments, four or more filters may be implemented. In oneembodiment, the method includes determining three or more additionalsets of parameter values for the analyte point estimate based on thereference analyte measurements. The three or more additional sets ofparameter values point to different sets of prior reference times.

In some embodiments, a second bank is implemented. In one embodiment,the method includes determining a fourth set of parameter values for asecond analyte point estimate. The second analyte point estimate isbased on a sum of an analyte point and a sum of a plurality of scaledrates-of-changes. Further, the analyte point corresponds to measurementsat an alternate initial reference time, wherein the alternate initialreference time is prior to the initial reference time by a time delay.The rates-of-changes include a third rate-of-change from the alternateinitial reference time to a fourth prior reference time with respect tothe alternate initial reference time, and a second rate-of-change fromthe alternate initial reference time to a fifth prior reference timewith respect to the alternate initial reference time. In some instances,the time delay is predetermined based on a projected duration ofartifacts.

In one embodiment, the method includes deriving a first set of weightingcoefficients from the first set of parameter values; calculating firstlag-compensated point measurements from the uncompensated analytemeasurements by applying the first set of weighting coefficients tocorresponding uncompensated analyte measurements received at the initialreference time, at the first prior reference time, and at the secondprior reference time of the first set of parameter values; deriving afourth set of weighting coefficients from the fourth set of parametervalues; calculating second lag-compensated point measurements from theuncompensated analyte measurements by applying the fourth set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the alternate initial reference time, at thefourth prior reference time, and at the fifth prior reference time ofthe fourth set of parameter values; determining a severity ofdiscrepancy between the first and the second analyte point estimates;and generating resulting lag-compensated point measurements based on aweighted sum of the first lag compensated point measurements and thesecond lag-compensated point measurements, wherein the weighted sum isbased on the severity of discrepancy.

In some embodiments, three or more banks may be implemented. In oneembodiment, the method includes determining additional sets of parametervalues for two or more additional analyte point estimates. The two ormore additional analyte point estimates are based on sums of an analytepoint and sums of a plurality of scaled rates-of-changes. The analytepoint corresponds to measurements at an alternate initial referencetime, wherein the alternate initial reference time is prior to theinitial reference time by a time delay. Further, the plurality of scaledrates-of-changes for each of the two or more additional analyte pointestimates are from the alternate initial reference time to fourth andfifth prior reference times with respect to the alternate initialreference time. Each set of reference times for the analyte pointestimate and the additional analyte point estimates is unique as awhole. In some instances, the time delay is predetermined based on aprojected duration of artifacts.

In one embodiment, the method includes deriving a first set of weightingcoefficients from the first set of parameter values; calculating firstlag-compensated point measurements from the uncompensated analytemeasurements by applying the first set of weighting coefficients tocorresponding uncompensated analyte measurements received at the initialreference time, at the first prior reference time, and at the secondprior reference time of the first set of parameter values; derivingadditional sets of weighting coefficients from the additional sets ofparameter values; calculating additional lag-compensated pointmeasurements from the uncompensated analyte measurements by applying theadditional sets of weighting coefficients to corresponding uncompensatedanalyte measurements received at the alternate initial reference time,and at the fourth and fifth prior reference times of each of theadditional sets of parameter values; determining a severity ofdiscrepancy between the analyte point estimates; and generatingresulting lag-compensated point measurements based on a weighted sum ofthe first lag compensated point measurements and the additionallag-compensated point measurements, wherein the weighted sum is based onthe severity of discrepancy.

In some embodiments, the analyte point estimate may include three ormore rates-of-changes. In one embodiment, the method includes one ormore additional rates-of-changes from the initial reference time toadditional prior reference times. In some instances, the first set ofparameter values include the first scalar of the first rate-of-change,the first prior reference time, the second scalar of the secondrate-of-change, the second prior reference time, additional scalars forthe additional rates-of-changes, and each of the additional priorreference times.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving weighting coefficients fromthe first set of parameter values, and calculating lag-compensated pointmeasurements from the uncompensated analyte measurements. Thelag-compensated point measurements are calculated by applying theweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, at the second prior reference time, and at each of theadditional prior reference times.

As stated above, in some aspects of the present disclosure, articles ofmanufacture for lag compensation of analyte point measurements areprovided. The articles of manufacture include a machine-readable mediumhaving machine-executable instructions stored thereon for lagcompensation of analyte measurements. The instructions includeinstructions for receiving reference analyte measurements, andinstructions for determining a first set of parameter values for ananalyte point estimate based on the reference analyte measurements. Theanalyte point estimate is based on a sum of an analyte point and a sumof a plurality of scaled rates-of-changes. The analyte point correspondsto measurements at an initial reference time. The rates-of-changesinclude a first rate-of-change from the initial reference time to afirst prior reference time, and a second rate-of-change from the initialreference time to a second prior reference time.

It should be appreciated that similar embodiments to those describedabove for the methods of lag compensation for analyte point measurementsare applicable to the articles of manufacture as well.

In some aspects of the present disclosure, methods of lag compensationfor analyte rate-of-change measurements are provided. The methodsinclude receiving reference analyte measurements, and determining afirst set of parameter values for an analyte rate-of-change estimatebased on the reference analyte measurements. The analyte rate-of-changeestimate is based on a sum of a plurality of scaled rates-of-changes.The rates-of-changes include a first rate-of-change from an initialreference time to a first prior reference time, and a secondrate-of-change from the initial reference time to a second priorreference time.

In one embodiment, the method includes receiving reference analytemeasurements, and determining a first set of parameter values for ananalyte rate-of-change estimate based on the reference analytemeasurements. The analyte rate-of-change estimate is based on a sum of aplurality of scaled rates-of-changes. The rates-of-changes include afirst rate-of-change from an initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time.

In one embodiment, the parameter values include a first scalar of thefirst rate-of-change, the first prior reference time, a second scalar ofthe second rate-of-change, and the second prior reference time.

In one embodiment, the determining of the first set of parameter valuesincludes calculating error metrics for a plurality of combinations ofvalues as parameters in the analyte rate-of-change estimate, andselecting the first set of parameter values based on the calculatederror metrics. In some instances, the error metrics are generated bycalculating a sum-of-squared-errors. In some instances, the first set ofparameter values is associated with a smallest error metric calculated.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, and calculatinglag-compensated rate-of-change measurements from the uncompensatedanalyte measurements. The lag-compensated rate-of-change measurementsare calculated by applying the first set of weighting coefficients tocorresponding uncompensated analyte measurements received at the initialreference time, at the first prior reference time, and at the secondprior reference time of the first set of parameter values.

In some embodiments, a second filter may be implemented. In oneembodiment, the method includes determining a second set of parametervalues for the analyte rate-of-change estimate based on the referenceanalyte measurements. The first set and the second set of parametervalues point to different sets of prior reference times.

In one embodiment, the determining of the first set and the second setof parameter values includes calculating error metrics for a pluralityof combinations of values as parameters in the analyte rate-of-changeestimate, and selecting the first set and the second set of parametervalues based on the calculated error metrics. In some instances, theerror metrics are generated by calculating a sum-of-squared-errors.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,and calculating lag-compensated rate-of-change measurements by averaginga first output and a second output. The first output is generated byapplying the first set of weighting coefficients to correspondinguncompensated analyte measurements received at the initial referencetime, at the first prior reference time, and at the second priorreference time of the first set of parameter values. Further, the secondoutput is generated by applying the second set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the second set of parameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, and deriving asecond set of weighting coefficients from the second set of parametervalues. When valid data is present at the first prior reference time andthe second prior reference time of the first set of parameter values,lag-compensated rate-of-change measurements are calculated from theuncompensated analyte measurements by applying the first set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the first setof parameter values. When valid data is present at the first priorreference time and the second prior reference time of the second set ofparameter values and not the first set of parameter values,lag-compensated rate-of-change measurements are calculated from theuncompensated analyte measurements by applying the second set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the second setof parameter values.

In some embodiments, a third filter may be implemented. In oneembodiment, the method includes determining a third set of parametervalues for the analyte rate-of-change estimate based on the referenceanalyte measurements. The first set, second set, and third set ofparameter values point to different sets of reference times.

In one embodiment, the determining of the first set, the second set, andthe third set of parameter values includes calculating error metrics fora plurality of combinations of values as parameters in the analyterate-of-change estimate, and selecting the first set, the second set,and the third set of parameter values based on the calculated errormetrics. In some instances, the error metrics are generated bycalculating a sum-of-squared-errors. The first set of parameter valuesis associated with a smaller calculated error metric than the second setof parameter values, and the second set of parameter values isassociated with a smaller calculated error metric than the third set ofparameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,deriving a third set of weighting coefficients from the third set ofparameter values, and calculating lag-compensated rate-of-changemeasurements by averaging available outputs, wherein the availableoutputs are generated by applying the first, second, and/or third setsof weighting coefficients to uncompensated analyte measurements receivedat the initial reference time and prior reference times of respectivefirst, second, and/or third sets of parameter values which have validdata present.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a secondset of weighting coefficients from the second set of parameter values,and deriving a third set of weighting coefficients from the third set ofparameter values. When valid data is present at the first priorreference time and the second prior reference time of the first set ofparameter values, lag-compensated rate-of-change measurements arecalculated from the uncompensated analyte measurements by applying thefirst set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, and at the second prior reference time ofthe first set of parameter values. When valid data is present at thefirst prior reference time and the second prior reference time of thesecond set of parameter values and not the first set of parametervalues, lag-compensated rate-of-change measurements are calculated fromthe uncompensated analyte measurements by applying the second set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the second setof parameter values. When valid data is present at the first priorreference time and the second prior reference time of the third set ofparameter values and not the first set or the second set of parametervalues, lag-compensated rate-of-change measurements are calculated fromthe uncompensated analyte measurements by applying the third set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the third setof parameter values.

In some embodiments, four or more filters may be implemented. In oneembodiment, the method includes determining three or more additionalsets of parameter values for the analyte rate-of-change estimate basedon the reference analyte measurements. The three or more additional setsof parameter values point to different sets of prior reference times.

In some embodiments, a second bank may be implemented. In oneembodiment, the method includes determining a fourth set of parametervalues for a second analyte rate-of-change estimate. The second analyterate-of-change estimate is based on a sum of a plurality of scaledrates-of-changes. The rates-of-changes include a third rate-of-changefrom an alternate initial reference time to a fourth prior referencetime with respect to the alternate initial reference time; and a fourthrate-of-change from the alternate initial reference time to a fifthprior reference time with respect to the alternate initial referencetime. The alternate initial reference time is prior to the initialreference time by a time delay. In some instances, the time delay ispredetermined based on a projected duration of artifacts.

In one embodiment, the method includes deriving a first set of weightingcoefficients from the first set of parameter values; calculating firstlag-compensated rate-of-change measurements from the uncompensatedanalyte measurements by applying the first set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the first set of parameter values;deriving a fourth set of weighting coefficients from the fourth set ofparameter values; calculating second lag-compensated rate-of-changemeasurements from the uncompensated analyte measurements by applying thefourth set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the alternate initial reference time,at the fourth prior reference time, and at the fifth prior referencetime of the fourth set of parameter values; determining a severity ofdiscrepancy between the first and the second analyte rate-of-changeestimates; generating resulting lag-compensated rate-of-changemeasurements based on a weighted sum of the first lag compensatedrate-of-change measurements and the second lag-compensatedrate-of-change measurements, wherein the weighted sum is based on theseverity of discrepancy.

In some embodiments, three or more banks are implemented. In oneembodiment, the method includes determining additional sets of parametervalues for two or more additional analyte rate-of-change estimates. Thetwo or more additional analyte rate-of-change estimates are based onsums of a plurality of scaled rates-of-changes. The plurality of scaledrates-of-changes for each of the two or more additional analyterate-of-change estimates are from an alternate initial reference time tofourth and fifth prior reference times with respect to the alternateinitial reference time, wherein each set of reference times for theanalyte rate-of-change estimate and the additional analyterate-of-change estimates is unique as a whole. The alternate initialreference time is prior to the initial reference time by a time delay.In some instances, the time delay is predetermined based on a projectedduration of artifacts.

In one embodiment, the method includes deriving a first set of weightingcoefficients from the first set of parameter values; calculating firstlag-compensated rate-of-change measurements from the uncompensatedanalyte measurements by applying the first set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the first set of parameter values;deriving additional sets of weighting coefficients from the additionalsets of parameter values; calculating additional lag-compensatedrate-of-change measurements from the uncompensated analyte measurementsby applying the additional sets of weighting coefficients tocorresponding uncompensated analyte measurements received at thealternate initial reference time, and at the fourth and fifth priorreference times of each of the additional sets of parameter values;determining a severity of discrepancy between the analyte rate-of-changeestimates; and generating resulting lag-compensated rate-of-changemeasurements based on a weighted sum of the first lag compensated outputand the additional lag-compensated outputs, wherein the weighted sum isbased on the severity of discrepancy.

In some embodiments, the analyte rate-of-change estimate includes threeor more rates-of-changes. In one embodiment, the method includes one ormore additional rates-of-changes from the initial reference time toadditional prior reference times.

In one embodiment, the first set of parameter values include the firstscalar of the first rate-of-change, the first prior reference time, thesecond scalar of the second rate-of-change, the second prior referencetime, additional scalars for the additional rates-of-changes, and eachof the additional prior reference times.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving weighting coefficients fromthe first set of parameter values, and calculating lag-compensatedrate-of-change measurements from the uncompensated analyte measurements.The lag-compensated rate-of-change measurements are calculated byapplying the weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, at the second prior reference time, and ateach of the additional prior reference times.

As stated above, in some aspects of the present disclosure, articles ofmanufacture for lag compensation of analyte rate-of-change measurementsare provided. The articles of manufacture include a machine-readablemedium having machine-executable instructions stored thereon for lagcompensation of analyte measurements. The instructions includeinstructions for receiving reference analyte measurements, andinstructions for determining a first set of parameter values for ananalyte rate-of-change estimate based on the reference analytemeasurements. The analyte rate-of-change estimate is based on a sum of aplurality of scaled rates-of-changes. The rates-of-changes include afirst rate-of-change from an initial reference time to a first priorreference time, and a second rate-of-change from the initial referencetime to a second prior reference time.

It should be appreciated that similar embodiments to those describedabove for the methods of lag compensation for analyte rate-of-changemeasurements are applicable to articles of manufacture as well.

In some aspects of the present disclosure, methods of lag compensationfor analyte point measurements and analyte rate-of-change measurementsare provided. The methods include receiving reference analytemeasurements, and determining a first set of parameter values for ananalyte point estimate based on the reference analyte measurements. Theanalyte point estimate is based on a sum of an analyte point and a sumof a first plurality of scaled rates-of-changes. The analyte pointcorresponds to measurements at an initial reference time. Therates-of-changes of the first plurality include a first rate-of-changefrom the initial reference time to a first prior reference time, and asecond rate-of-change from the initial reference time to a second priorreference time. The methods also include determining a second set ofparameter values for an analyte rate-of-change estimate based on thereference analyte measurements. The analyte rate-of-change estimate isbased on the sum of a second plurality of scaled rates-of-changes. Therates-of-changes of the second plurality include a third rate-of-changefrom an initial reference time to a third prior reference time, and afourth rate-of-change from the initial reference time to a fourth priorreference time.

In one embodiment, the method includes receiving reference analytemeasurements, and determining a first set of parameter values for ananalyte point estimate based on the reference analyte measurements. Theanalyte point estimate is based on a sum of an analyte point, and a sumof a first plurality of scaled rates-of-changes, The analyte pointcorresponds to measurements at an initial reference time. Therates-of-changes of the first plurality include a first rate-of-changefrom the initial reference time to a first prior reference time, and asecond rate-of-change from the initial reference time to a second priorreference time. Furthermore, the method includes determining a secondset of parameter values for an analyte rate-of-change estimate based onthe reference analyte measurements. The analyte rate-of-change estimateis based on the sum of a second plurality of scaled rates-of-changes.The rates-of-changes of the second plurality include a thirdrate-of-change from an initial reference time to a third prior referencetime, and a fourth rate-of-change from the initial reference time to afourth prior reference time.

In one embodiment, the first prior reference time is equal to the thirdprior reference time, and the second prior reference time is equal tothe fourth prior reference time.

In one embodiment, the first set of parameter values for the analytepoint estimate includes a first scalar of the first rate-of-change, thefirst prior reference time, a second scalar of the secondrate-of-change, and the second prior reference time. Further, the secondset of parameter values for the analyte rate-of-change estimate includesa third scalar of the third rate-of-change, the third prior referencetime, a fourth scalar of the fourth rate-of-change, and the fourth priorreference time.

In one embodiment, the determining of the first set of parameter valuesand the determining of the second set of parameter values includescalculating error metrics for a plurality of combinations of values asparameters in the analyte point estimate and the analyte rate-of-changeestimate, and selecting the first set of parameter values and second setof parameter values based on the calculated error metrics. In someinstances, the error metrics are generated by calculating asum-of-squared-errors. In some instances, the first set of parametervalues and second set of parameter values are associated with a smallesterror metric calculated.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements; deriving a first set of weightingcoefficients from the first set of parameter values; deriving a secondset of weighting coefficients from the second set of parameter values;and calculating lag-compensated point measurements from theuncompensated analyte measurements, wherein the lag-compensated pointmeasurements are calculated by applying the first set of weightingcoefficients to corresponding uncompensated analyte measurementsreceived at the initial reference time, at the first prior referencetime, and at the second prior reference time of the first set ofparameter values; and calculating lag-compensated rate-of-changemeasurements from the uncompensated analyte measurements, wherein thelag-compensated rate-of-change measurements are calculated by applyingthe second set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thethird prior reference time, and at the fourth prior reference time ofthe second set of parameter values.

In some embodiments, a second filter is implemented. In one embodiment,the method includes determining a third set of parameter values for theanalyte point estimate based on the reference analyte measurements. Thefirst set and the third set of parameter values of the analyte pointestimate point to different sets of prior reference times. The methodfurther includes determining a fourth set of parameter values for theanalyte rate-of-change estimate based on the reference analytemeasurements. The second set and the fourth set of parameter values ofthe analyte rate-of-change estimate point to different sets of priorreference times.

In one embodiment, the method includes determining of the first set,second set, third set, and fourth set of parameter values includescalculating error metrics for a plurality of combinations of values asparameters in the analyte point estimates and analyte rate-of-changeestimates, and selecting the first set, second set, third set, andfourth set of parameter values based on the calculated error metrics. Insome instances, the error metrics are generated by calculating asum-of-squared-errors, the first set of parameter values is associatedwith a smaller calculated error metric than the third set of parametervalues, and the second set of parameter values is associated with asmaller calculated error metric than the fourth set of parameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a thirdset of weighting coefficients from the third set of parameter values;and calculating lag-compensated point measurements by averaging a firstoutput and a third output. The first output is generated by applying thefirst set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, and at the second prior reference time ofthe first set of parameter values. The third output is generated byapplying the third set of weighting coefficients to correspondinguncompensated analyte measurements received at the initial referencetime, at the first prior reference time, and at the second priorreference time of the third set of parameter values. The method furtherincludes deriving a second set of weighting coefficients from the secondset of parameter values, deriving a fourth set of weighting coefficientsfrom the fourth set of parameter values, and calculating lag-compensatedrate-of-change measurements by averaging a second output and a fourthoutput. The second output is generated by applying the second set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the second setof parameter values. The fourth output is generated by applying thefourth set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thethird prior reference time, and at the fourth prior reference time ofthe fourth set of parameter values.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, and deriving athird set of weighting coefficients from the third set of parametervalues. When valid data is present at the first prior reference time andthe second prior reference time of the first set of parameter values,lag-compensated point measurements are calculated from the uncompensatedanalyte measurements by applying the first set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the first set of parameter values. Whenvalid data is present at the first prior reference time and the secondprior reference time of the second set of parameter values and not thefirst set of parameter values, lag-compensated point measurements arecalculated from the uncompensated analyte measurements by applying thethird set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thefirst prior reference time, and at the second prior reference time ofthe third set of parameter values. The method further includes derivinga second set of weighting coefficients from the second set of parametervalues, and deriving a fourth set of weighting coefficients from thefourth set of parameter values. When valid data is present at the thirdprior reference time and the fourth prior reference time of the secondset of parameter values, lag-compensated rate-of-change measurements arecalculated from the uncompensated analyte measurements by applying thesecond set of weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thethird prior reference time, and at the fourth prior reference time forthe second set of parameter values. When valid data is present at thethird prior reference time and the fourth prior reference time for thefourth set of parameter values and not the second set of parametervalues, lag-compensated rate-of-change measurements are calculated fromthe uncompensated analyte measurements by applying the fourth set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the fourth setof parameter values.

In some embodiments, a third filter is implemented. In one embodiment,the method includes determining a fifth set of parameter values for theanalyte point estimate based on the reference analyte measurements. Thefirst set, the third set, and the fifth set of parameter values point todifferent sets of prior reference times. The method further includesdetermining a sixth set of parameter values for the analyterate-of-change estimate based on the reference analyte measurements. Thesecond set, the fourth set, and the sixth set of parameter values pointto different sets of prior reference times.

In one embodiment, the determining of the first set, second set, thirdset, fourth set, fifth set, and sixth set of parameter values includescalculating error metrics for a plurality of combinations of values asparameters in the analyte point estimates and the analyte rate-of-changeestimates, and selecting the first set, second set, third set, fourthset, fifth set, and sixth set of parameter values based on thecalculated error metrics. In some instances, the error metrics aregenerated by calculating a sum-of-squared-errors, the first set ofparameter values is associated with a smaller calculated error metricthan the third set of parameter values, the third set of parametervalues is associated with a smaller calculated error metric than thefifth set of parameter values, the second set of parameter values isassociated with a smaller calculated error metric than the fourth set ofparameter values, and the fourth set of parameter values is associatedwith a smaller calculated error metric than the sixth set of parametervalues.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements; deriving a first set of weightingcoefficients from the first set of parameter values; deriving a thirdset of weighting coefficients from the third set of parameter values;deriving a fifth set of weighting coefficients from the fifth set ofparameter values; calculating lag-compensated point measurements byaveraging available outputs, wherein the available outputs are generatedby applying the first, third, and/or fifth sets of weightingcoefficients to uncompensated analyte measurements received at theinitial reference time, the first prior reference time, and the secondprior reference time of respective first, third, and/or fifth sets ofparameter values which have valid data present; deriving a second set ofweighting coefficients from the second set of parameter values; derivinga fourth set of weighting coefficients from the fourth set of parametervalues; deriving a sixth set of weighting coefficients from the sixthset of parameter values; and calculating lag-compensated rate-of-changemeasurements by averaging available outputs, wherein the availableoutputs are generated by applying the second, fourth, and/or sixth setsof weighting coefficients to uncompensated analyte measurements receivedat the initial reference time, the third prior reference time, and thefourth prior reference time of respective first, second, and/or thirdsets of parameter values which have valid data present.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving a first set of weightingcoefficients from the first set of parameter values, deriving a thirdset of weighting coefficients from the third set of parameter values,and deriving a fifth set of weighting coefficients from the fifth set ofparameter values. When valid data is present at the first priorreference time and the second prior reference time of the first set ofparameter values, lag-compensated point measurements are calculated fromthe uncompensated analyte measurements by applying the first set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, and at the second prior reference time of the first setof parameter values. When valid data is present at the first priorreference time and the second prior reference time of the third set ofparameter values and not the first set of parameter values,lag-compensated point measurements are calculated from the uncompensatedanalyte measurements by applying the third set of weighting coefficientsto corresponding uncompensated analyte measurements received at theinitial reference time, at the first prior reference time, and at thesecond prior reference time of the third set of parameter values. Whenvalid data is present at the first prior reference time and the secondprior reference time of the fifth set of parameter values and not thefirst set or the third set of parameter values, lag-compensated pointmeasurements are calculated from the uncompensated analyte measurementsby applying the fifth set of weighting coefficients to correspondinguncompensated analyte measurements received at the initial referencetime, at the first prior reference time, and at the second priorreference time of the fifth set of parameter values. The method furtherincludes deriving a second set of weighting coefficients from the secondset of parameter values, deriving a fourth set of weighting coefficientsfrom the fourth set of parameter values, and deriving a sixth set ofweighting coefficients from the fifth set of parameter values. Whenvalid data is present at the third prior reference time and the fourthprior reference time of the second set of parameter values,lag-compensated rate-of-change measurements are calculated from theuncompensated analyte measurements by applying the second set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the second setof parameter values. When valid data is present at the third priorreference time and the fourth prior reference time of the fourth set ofparameter values and not the second set of parameter values,lag-compensated rate-of-change measurements are calculated from theuncompensated analyte measurements by applying the fourth set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the fourth setof parameter values. When valid data is present at the third priorreference time and the fourth prior reference time of the sixth set ofparameter values and not the second set or the fourth set of parametervalues, lag-compensated rate-of-change measurements are calculated fromthe uncompensated analyte measurements by applying the sixth set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the sixth setof parameter values.

In some embodiments, four or more filters are implemented. In oneembodiment, the method includes determining three or more additionalsets of parameter values for the analyte point estimate based on thereference analyte measurements, wherein the three or more additionalsets of parameter values point to different sets of prior referencetimes; and/or determining three or more additional sets of parametervalues for the analyte rate-of-change estimate based on the referenceanalyte measurements, wherein the three or more additional sets ofparameter values point to different sets of prior reference times.

In some embodiments, a second bank of an analyte point estimate isimplemented. In one embodiment, the method includes determining aseventh set of parameter values for a second analyte point estimate. Thesecond analyte point estimate is based on a sum of an analyte point anda sum of a third plurality of scaled rates-of-changes. The analyte pointcorresponds to measurements at an first alternate initial referencetime, wherein the first alternate initial reference time is prior to theinitial reference time by a first time delay. The rates-of-changes ofthe third plurality include a fifth rate-of-change from the firstalternate initial reference time to a fifth prior reference time withrespect to the first alternate initial reference time, and a sixthrate-of-change from the first alternate initial reference time to asixth prior reference time with respect to the first alternate initialreference time.

In one embodiment, the method includes deriving a first set of weightingcoefficients from the first set of parameter values; calculating firstlag-compensated point measurements from the uncompensated analytemeasurements by applying the first set of weighting coefficients tocorresponding uncompensated analyte measurements received at the initialreference time, at the first prior reference time, and at the secondprior reference time of the first set of parameter values; deriving aseventh set of weighting coefficients from the seventh set of parametervalues; calculating second lag-compensated point measurements from theuncompensated analyte measurements by applying the seventh set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the first alternate initial reference time, atthe fifth prior reference time, and at the sixth prior reference time ofthe seventh set of parameter values; determining a severity ofdiscrepancy between the first and the second analyte point estimates;and generating resulting lag-compensated point measurements based on aweighted sum of the first lag compensated output and the secondlag-compensated output, wherein the weighted sum is based on theseverity of discrepancy.

In some embodiments, a second bank of analyte rate-of-change estimate isimplemented. In one embodiment, the method includes determining aneighth set of parameter values for a second analyte rate-of-changeestimate. The second analyte rate-of-change estimate is based on a sumof a fourth plurality of scaled rates-of-changes. The rates-of-changesof the fourth plurality include a seventh rate-of-change from a secondalternate initial reference time to an seventh prior reference time withrespect to the second alternate initial reference time, and an eighthrate-of-change from the second alternate initial reference time to aneighth prior reference time with respect to the second alternate initialreference time. The second alternate initial reference time is prior tothe initial reference time by a second time delay.

In one embodiment, the method includes deriving a second set ofweighting coefficients from the second set of parameter values;calculating first lag-compensated rate-of-change measurements from theuncompensated analyte measurements by applying the second set ofweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the third priorreference time, and at the fourth prior reference time of the second setof parameter values; deriving an eighth set of weighting coefficientsfrom the eighth set of parameter values; calculating secondlag-compensated rate-of-change measurements from the uncompensatedanalyte measurements by applying the eighth set of weightingcoefficients to corresponding uncompensated analyte measurementsreceived at the second alternate initial reference time, at the seventhprior reference time, and at the eighth prior reference time of theeighth set of parameter values; determining a severity of discrepancybetween the first and the second analyte rate-of-change estimates; andgenerating resulting lag-compensated rate-of-change measurements basedon a weighted sum of the first lag compensated rate-of-changemeasurements and the second lag-compensated rate-of-change measurements,wherein the weighting is based on the severity of discrepancy.

In some instances, the first time delay and/or second time delay ispredetermined based on a projected size of artifacts.

In some embodiments, the analyte point estimate includes three or morerates-of-changes. In one embodiment, the rates-of-changes of the firstplurality include one or more additional rates-of-changes from theinitial reference time to additional prior reference times.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving weighting coefficients fromthe first set of parameter values, and calculating lag-compensated pointmeasurements from the uncompensated analyte measurements. Thelag-compensated point measurements are calculated by applying theweighting coefficients to corresponding uncompensated analytemeasurements received at the initial reference time, at the first priorreference time, at the second prior reference time, and at each of theadditional prior reference times.

In some embodiments, the analyte rate-of-change estimate includes threeor more rates-of-changes. In one embodiment, the rates-of-changes of thesecond plurality include one or more additional rates-of-changes fromthe initial reference time to additional prior reference times.

In one embodiment, the method includes receiving a series ofuncompensated analyte measurements, deriving weighting coefficients fromthe second set of parameter values, and calculating lag-compensatedrate-of-change measurements from the uncompensated analyte measurements.The lag-compensated rate-of-change measurements are calculated byapplying the weighting coefficients to corresponding uncompensatedanalyte measurements received at the initial reference time, at thethird prior reference time, at the fourth prior reference time, and ateach of the additional prior reference times.

As stated above, in some aspects of the present disclosure, articles ofmanufacture for lag compensation of analyte point measurements andanalyte rate-of-change measurements are provided. The articles ofmanufacture include a machine-readable medium having machine-executableinstructions stored thereon for lag compensation of analytemeasurements. The instructions include instructions for receivingreference analyte measurements, and instructions for determining a firstset of parameter values for an analyte point estimate based on thereference analyte measurements. The analyte point estimate is based on asum of an analyte point and a sum of a first plurality of scaledrates-of-changes. The analyte point corresponds to measurements at aninitial reference time. The rates-of-changes of the first pluralityinclude a first rate-of-change from the initial reference time to afirst prior reference time and a second rate-of-change from the initialreference time to a second prior reference time. The articles ofmanufacture also include instructions for determining a second set ofparameter values for an analyte rate-of-change estimate based on thereference analyte measurements. The analyte rate-of-change estimate isbased on the sum of a second plurality of scaled rates-of-changes. Therates-of-changes of the second plurality include a third rate-of-changefrom an initial reference time to a third prior reference time, and afourth rate-of-change from the initial reference time to a fourth priorreference time.

It should be appreciated that similar embodiments to those describedabove for the methods of lag compensation for analyte point measurementsand analyte rate-of-change measurements are applicable to articles ofmanufacture as well.

It should be understood that techniques introduced above can beimplemented by programmable circuitry programmed or configured bysoftware and/or firmware, or they can be implemented entirely byspecial-purpose “hardwired” circuitry, or in a combination of suchforms. Such special-purpose circuitry (if any) can be in the form of,for example, one or more application-specific integrated circuits(ASICS), programmable logic devices (PLDs), field-programmable gatearrays (FPGAs), etc.

Software or firmware implementing the techniques introduced herein maybe stored on a machine-readable storage medium and may be executed byone or more general-purpose or special-purpose programmablemicroprocessors. A “machine-readable medium”, as the term is usedherein, includes any mechanism that can store information in a formaccessible by a machine (a machine may be, for example, a computer,network device, cellular phone, personal digital assistant (PDA),manufacturing took, any device with one or more processors, etc.). Forexample, a machine-accessible medium includes recordable/non-recordablemedia (e.g., read-only memory (ROM); random access memory (RAM);magnetic disk storage media; optical storage media; flash memorydevices; etc.), etc.

Furthermore, a data processing device or system, such as a computer orcomputer system may be configured to execute some of the techniquesintroduced herein. The computer may include, for example, a processingdevice, memory with instructions stored therein to perform thetechniques, input/output device elements (e.g., a monitor, keyboard,etc.), etc. For example, the device or system may be used to configure,calibrate, or otherwise program an analyte monitoring device intended toperform analyte measurements, such as analyte point measurements and/oranalyte rate-of-change measurements. In some aspects of the presentdisclosure, some of the techniques described herein may be provided tothe device or system from an article of manufacture including themachine readable medium described above.

The preceding examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the embodiments of the invention, and are not intended tolimit the scope of what the inventors regard as their invention nor arethey intended to represent that the experiments below are all or theonly experiments performed. Efforts have been made to ensure accuracywith respect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

1. A method comprising: applying a first analyte point measurementfilter, the first analyte point measurement filter comprising:receiving, from an in vivo analyte sensor, at least a firstuncompensated analyte measurement at a first initial reference time, asecond uncompensated analyte measurement at a first prior referencetime, and a third uncompensated analyte measurement at a second priorreference time; determining a first scaled rate-of-change by multiplyinga first weighting coefficient and a first rate-of-change, the firstrate-of-change computed between the first uncompensated analytemeasurement at the first initial reference time to the seconduncompensated analyte measurement at the first prior reference time;determining a second scaled rate-of-change by multiplying a secondweighting coefficient and a second rate-of-change, the secondrate-of-change computed between the first uncompensated analytemeasurement at the first initial reference time to the thirduncompensated analyte measurement at the second prior reference time;and calculating a first filter lag-compensated point measurement basedon the sum of the first uncompensated analyte measurement, the firstscaled rate-of-change, and the second scaled rate-of-change.
 2. Themethod of claim 1, wherein the in vivo analyte sensor detects glucose.3. The method of claim 1, further comprising calculating a first filterlag-compensated rate-of-change analyte measurement based on the sum ofthe first scaled rate-of-change and the second scaled rate-of-change. 4.The method of claim 1, further comprising: determining a first filterscaled lag-compensated analyte point measurement by multiplying a firstfilter point weighting coefficient and the first filter lag-compensatedanalyte point measurement; applying at least a second analyte pointmeasurement filter, the second analyte point measurement filtercomprising: receiving, from an in vivo analyte sensor, at least a fourthuncompensated analyte measurement at a fourth prior reference timerelative to the first initial reference time, and a fifth uncompensatedanalyte measurement at a fifth prior reference time relative to thefirst initial reference time; determining a third scaled rate-of-changeby multiplying a third weighting coefficient and a third rate-of-change,the third rate-of-change computed between the first uncompensatedanalyte measurement at the first initial reference time to the fourthuncompensated analyte measurement at the fourth prior reference time;determining a fourth scaled rate-of-change by multiplying a fourthweighting coefficient and a fourth rate-of-change, the fourthrate-of-change computed between the first uncompensated analytemeasurement at the first initial reference time to the fifthuncompensated analyte measurement at the fifth prior reference time;calculating a second filter lag-compensated analyte point measurementbased on the sum of the first uncompensated analyte measurement, thethird scaled rate-of-change, and the fourth scaled rate of change; anddetermining a second filter scaled lag-compensated analyte pointmeasurement by multiplying a second filter point weighting coefficientand the second filter lag-compensated analyte point measurement; andcalculating a first bank lag-compensated analyte point measurement basedon the sum of the first filter scaled lag-compensated analyte pointmeasurement and the second filter scaled lag-compensated analytemeasurement.
 5. The method of claim 4, wherein the in vivo analytesensor detects glucose.
 6. The method of claim 4, further comprising:calculating a first filter lag-compensated analyte rate-of-changemeasurement based on the sum of the first scaled rate-of-change and thesecond scaled rate-of-change; determining a first filter scaledlag-compensated analyte rate-of-change measurement by multiplying afirst filter rate-of-change weighting coefficient and the first filterlag-compensated analyte rate-of-change measurement; calculating a secondfilter lag-compensated analyte rate-of-change measurement based on thesum of the third scaled rate-of-change and the fourth scaledrate-of-change; determining a second filter scaled lag-compensatedanalyte rate-of-change measurement by multiplying a second filterrate-of-change weighting coefficient and the second filterlag-compensated analyte rate-of-change measurement; and calculating alag-compensated rate-of-change analyte measurement based on the sum ofthe first filter scaled analyte rate-of-change measurement and thesecond filter scaled analyte rate-of-change measurement.
 7. The methodof claim 1, further comprising: determining a first filter scaledlag-compensated analyte point measurement by multiplying a first filterpoint weighting coefficient and the first filter lag-compensated analytepoint measurement; applying a second analyte point measurement filter,the second analyte point measurement filter comprising: receiving, froman in vivo analyte sensor, at least a fourth uncompensated analytemeasurement at a fourth prior reference time relative to the firstinitial reference time, and a fifth uncompensated analyte measurement ata fifth prior reference time relative to the first initial referencetime; determining a third scaled rate-of-change by multiplying a thirdweighting coefficient and a third rate-of-change, the thirdrate-of-change computed between the first uncompensated analytemeasurement at the first initial reference time to the fourthuncompensated analyte measurement at the fourth prior reference time;determining a fourth scaled rate-of-change by multiplying a fourthweighting coefficient and a fourth rate-of-change, the fourthrate-of-change computed between the first uncompensated analytemeasurement at the first initial reference time to the fifthuncompensated analyte measurement at the fifth prior reference time;calculating a second filter lag-compensated analyte point measurementbased on the sum of the first uncompensated analyte measurement, thethird scaled rate-of-change, and the fourth scaled rate of change; anddetermining a second filter scaled lag-compensated analyte pointmeasurement by multiplying a second filter point weighting coefficientand the second filter lag-compensated analyte point measurement;applying at least a third analyte point measurement filter, the thirdanalyte point measurement filter comprising: receiving, from an in vivoanalyte sensor, at least a sixth uncompensated analyte measurement at asixth prior reference time relative to the first initial reference time,and a seventh uncompensated analyte measurement at a seventh priorreference time relative to the first initial reference time; determininga fifth scaled rate-of-change by multiplying a fifth weightingcoefficient and a fifth rate-of-change, the fifth rate-of-changecomputed between the first uncompensated analyte measurement at thefirst initial reference time to the sixth uncompensated analytemeasurement at the sixth prior reference time; determining a sixthscaled rate-of-change by multiplying a sixth weighting coefficient and asixth rate-of-change, the sixth rate-of-change computed between thefirst uncompensated analyte measurement at the first initial referencetime to the seventh uncompensated analyte measurement at the seventhprior reference time; calculating a third filter lag-compensated analytepoint measurement based on the sum of the first uncompensated analytemeasurement, the fifth scaled rate-of-change, and the sixth scaled rateof change; and determining a third filter scaled lag-compensated analytepoint measurement by multiplying a third filter point weightingcoefficient and the third filter lag-compensated analyte pointmeasurement; and calculating the first bank lag-compensated analytepoint measurement based on the sum of the first filter scaledlag-compensated analyte point measurement, the second filter scaledlag-compensated analyte point measurement, and the third filter scaledlag-compensated analyte point measurement.
 8. The method of claim 7,wherein the in vivo analyte sensor detects glucose.
 9. The method ofclaim 7, further comprising: calculating a first filter lag-compensatedanalyte rate-of-change measurement based on the sum of the first scaledrate-of-change and the second scaled rate-of-change; determining a firstfilter scaled lag-compensated analyte rate-of-change measurement bymultiplying a first filter rate-of-change weighting coefficient and thefirst filter lag-compensated analyte rate-of-change measurement;calculating a second filter lag-compensated analyte rate-of-changemeasurement based on the sum of the third scaled rate-of-change and thefourth scaled rate-of-change; determining a second filter scaledlag-compensated analyte rate-of-change measurement by multiplying asecond filter rate-of-change weighting coefficient and the second filterlag-compensated analyte rate-of-change measurement; calculating a thirdfilter lag-compensated analyte rate-of-change measurement based on thesum of the fifth scaled rate-of-change and the sixth scaled rate ofchange; and determining a third filter scaled lag-compensated analyterate-of-change measurement by multiplying a third filter rate-of-changeweighting coefficient and the third filter lag-compensated analyterate-of-change measurement; and calculating a lag-compensatedrate-of-change analyte measurement based on the sum of the first filterscaled analyte rate-of-change measurement, the second filter scaledanalyte rate-of-change measurement, and the third filter scaled analyterate-of-change measurement.
 10. The method of claim 7, furthercomprising calculating at least a second bank lag-compensated analytepoint measurement by applying at least a fourth analyte pointmeasurement filter, the fourth analyte point measurement filtercomprising: receiving, from the in vivo analyte sensor, at least aneighth uncompensated analyte measurement at a second initial referencetime that is prior to the first initial reference time, a ninthuncompensated analyte measurement at an eighth prior reference time, anda tenth uncompensated analyte measurement at a ninth prior referencetime, wherein the eight prior reference time and the ninth priorreference time are relative to the second initial reference time;determining a seventh scaled rate-of-change by multiplying a seventhweighting coefficient and a seventh rate-of-change, the seventhrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the ninthuncompensated analyte measurement at the eighth prior reference time;determining an eighth scaled rate-of-change by multiplying an eighthweighting coefficient and an eighth rate-of-change, the eighthrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the tenthuncompensated analyte measurement at the ninth prior reference time; andcalculating a fourth filter lag-compensated analyte point measurementbased on the sum of the eighth uncompensated analyte measurement, theseventh scaled rate-of-change, and the eighth scaled rate-of-change. 11.The method of claim 10, wherein the in vivo analyte sensor detectsglucose.
 12. The method of claim 10, further comprising: determining aweighted first bank lag-compensated analyte point measurement bymultiplying the first bank lag-compensated analyte point measurement bya first discrepancy weighting agent; determining a weighted second banklag-compensated analyte point measurement by multiplying the fourthfilter lag-compensated analyte point measurement by a second discrepancyweighting agent, wherein the first discrepancy weighting agent and thesecond discrepancy weighting agent are based on a severity ofdiscrepancy between the first bank lag-compensated analyte pointmeasurement and the fourth filter lag-compensated analyte pointmeasurement; and calculating a resulting lag-compensated analyte pointmeasurement based on the sum of the weighted first bank lag-compensatedanalyte point measurement and the weighted second bank lag-compensatedanalyte point measurement.
 13. The method of claim 10, furthercomprising: determining a fourth filter scaled lag-compensated analytepoint measurement by multiplying a fourth filter point weightingcoefficient and the fourth filter lag-compensated analyte pointmeasurement; calculating the second bank lag-compensated analyte pointmeasurement by applying at least a fifth analyte point measurementfilter, the fifth analyte point measurement filter comprising:receiving, from the in vivo analyte sensor, at least an eleventhuncompensated analyte measurement at a tenth prior reference time, and atwelfth uncompensated analyte measurement at an eleventh prior referencetime, wherein the tenth prior reference time and the eleventh priorreference time are relative to the second initial reference time;determining a ninth scaled rate-of-change by multiplying a ninthweighting coefficient and a ninth rate-of-change, the ninthrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the eleventhuncompensated analyte measurement at the tenth prior reference time;determining a tenth scaled rate-of-change by multiplying a tenthweighting coefficient and a tenth rate-of-change, the tenthrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the twelfthuncompensated analyte measurement at the tenth prior reference time;calculating a fifth filter lag-compensated analyte point measurementbased on the sum of the eighth uncompensated analyte measurement, theninth scaled rate-of-change, and the tenth scaled rate-of-change; anddetermining a fifth filter scaled lag-compensated analyte pointmeasurement by multiplying a fifth filter point weighting coefficientand the fifth filter lag-compensated analyte point measurement; andcalculating the second bank lag-compensated analyte point measurementbased on the sum of the fourth filter scaled lag-compensated analytepoint measurement and the fifth filter scaled lag-compensated analytepoint measurement.
 14. The method of claim 13, wherein the in vivoanalyte sensor detects glucose.
 15. The method of claim 13, furthercomprising: determining a weighted first bank lag-compensated analytepoint measurement by multiplying the first bank lag-compensated analytepoint measurement by a first discrepancy weighting agent; determining aweighted second bank lag-compensated analyte point measurement bymultiplying the second bank lag-compensated analyte point measurement bya second discrepancy weighting agent, wherein the first discrepancyweighting agent and the second discrepancy weighting agent are based ona severity of discrepancy between the first bank lag-compensated analytepoint measurement and the second bank lag-compensated analyte pointmeasurement; and calculating a resulting lag-compensated analyte pointmeasurement based on the sum of the weighted first bank lag-compensatedanalyte point measurement and the weighted second bank lag-compensatedanalyte point measurement.
 16. The method of claim 10, furthercomprising: determining a fourth filter scaled lag-compensated analytepoint measurement by multiplying a fourth filter point weightingcoefficient and the fourth filter lag-compensated analyte pointmeasurement; calculating the second bank lag-compensated analyte pointmeasurement comprising: applying a fifth analyte point measurementfilter, the fifth analyte point measurement filter comprising:receiving, from the in vivo analyte sensor, at least an eleventhuncompensated analyte measurement at a tenth prior reference time, and atwelfth uncompensated analyte measurement at an eleventh prior referencetime, wherein the tenth prior reference time and the eleventh priorreference time are relative to the second initial reference time;determining a ninth scaled rate-of-change by multiplying a ninthweighting coefficient and a ninth rate-of-change, the ninthrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the eleventhuncompensated analyte measurement at the tenth prior reference time;determining a tenth scaled rate-of-change by multiplying a tenthweighting coefficient and a tenth rate-of-change, the tenthrate-of-change computed between the eighth uncompensated analytemeasurement at the second initial reference time to the twelfthuncompensated analyte measurement at the tenth prior reference time;calculating a fifth filter lag-compensated analyte point measurementbased on the sum of the eighth uncompensated analyte measurement, theninth scaled rate-of-change, and the tenth scaled rate-of-change; anddetermining a fifth filter scaled lag-compensated analyte pointmeasurement by multiplying a fifth filter point weighting coefficientand the fifth filter lag-compensated analyte point measurement; applyingat least a sixth analyte point measurement filter, the sixth analytepoint measurement filter comprising: receiving, from the in vivo analytesensor, at least a thirteenth uncompensated analyte measurement at atwelfth prior reference time, and a fourteenth uncompensated analytemeasurement at a thirteenth prior reference time, wherein the twelfthprior reference time and the thirteenth prior reference time arerelative to the second initial reference time; determining an eleventhscaled rate-of-change by multiplying an eleventh weighting coefficientand an eleventh rate-of-change, the eleventh rate-of-change computedbetween the eighth uncompensated analyte measurement at the secondinitial reference time to the thirteenth uncompensated analytemeasurement at the twelfth prior reference time; determining a twelfthscaled rate-of-change by multiplying a twelfth weighting coefficient anda twelfth rate-of-change, the twelfth rate-of-change computed betweenthe eighth uncompensated analyte measurement at the second initialreference time to the fourteenth uncompensated analyte measurement atthe thirteenth prior reference time; calculating a sixth filterlag-compensated analyte point measurement based on the sum of the eighthuncompensated analyte measurement, the eleventh scaled rate-of-change,and the twelfth scaled rate-of-change; and determining a sixth filterscaled lag-compensated analyte point measurement by multiplying a sixthfilter point weighting coefficient and the sixth filter lag-compensatedanalyte point measurement; and calculating the second banklag-compensated analyte point measurement based on the sum of the fourthfilter scaled lag-compensated analyte point measurement, the fifthfilter scaled lag-compensated analyte point measurement, and the sixthfilter scaled lag-compensated analyte point measurement.
 17. The methodof claim 16, wherein the in vivo analyte sensor detects glucose.
 18. Themethod of claim 16, further comprising: determining a weighted firstbank lag-compensated analyte point measurement by multiplying the firstbank lag-compensated analyte point measurement by a first discrepancyweighting agent; determining a weighted second bank lag-compensatedanalyte point measurement by multiplying the second bank lag-compensatedanalyte point measurement by a second discrepancy weighting agent,wherein the first discrepancy weighting agent and the second discrepancyweighting agent are based on a severity of discrepancy between the firstbank lag-compensated analyte point measurement and the second banklag-compensated analyte point measurement; and calculating a resultinglag-compensated analyte point measurement based on the sum of theweighted first bank lag-compensated analyte point measurement and theweighted second bank lag-compensated analyte point measurement.